From 854ce30241d6fa78e0f49a60aa7507daea76b49b Mon Sep 17 00:00:00 2001
From: Erik Recio <erikrecio@gmail.com>
Date: Wed, 11 Dec 2024 16:39:32 +0100
Subject: [PATCH 1/7] tutorial iqpopt

---
 _static/authors/erik_recio.jpg                | Bin 0 -> 417476 bytes
 _static/authors/erik_recio.txt                |   4 +
 .../iqpopt/iqpopt_main.png                    | Bin 0 -> 253431 bytes
 demonstrations/tutorial_iqpopt.py             | 645 ++++++++++++++++++
 4 files changed, 649 insertions(+)
 create mode 100644 _static/authors/erik_recio.jpg
 create mode 100644 _static/authors/erik_recio.txt
 create mode 100644 _static/demonstration_assets/iqpopt/iqpopt_main.png
 create mode 100644 demonstrations/tutorial_iqpopt.py

diff --git a/_static/authors/erik_recio.jpg b/_static/authors/erik_recio.jpg
new file mode 100644
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diff --git a/_static/authors/erik_recio.txt b/_static/authors/erik_recio.txt
new file mode 100644
index 0000000000..ee45facd25
--- /dev/null
+++ b/_static/authors/erik_recio.txt
@@ -0,0 +1,4 @@
+.. bio:: Erik Recio
+   :photo: ../_static/authors/erik_recio.jpg
+
+   Erik is a quantum researcher at ICFO and is doing his PhD on Quantum Machine Learning.
\ No newline at end of file
diff --git a/_static/demonstration_assets/iqpopt/iqpopt_main.png b/_static/demonstration_assets/iqpopt/iqpopt_main.png
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diff --git a/demonstrations/tutorial_iqpopt.py b/demonstrations/tutorial_iqpopt.py
new file mode 100644
index 0000000000..4d7db84349
--- /dev/null
+++ b/demonstrations/tutorial_iqpopt.py
@@ -0,0 +1,645 @@
+r"""IQPopt: Fast optimization of IQP circuits in JAX
+
+================================================
+
+"""
+
+######################################################################
+# The Pennylane library offers the possibility of simulating any type of quantum circuit, but the
+
+# resources needed always scale exponentially with the number of qubits. There are some classes of
+
+# quantum citcuits that we know can be efficiently simulated, and this work attempts to fulfil part of
+
+# this niche.
+
+# 
+
+# IQPopt is a package designed for fast optimization of parameterized Instantaneous Quantum Polynomial
+
+# (IQP) circuits using JAX, resulting in a linear scaling on the number of qubits. These circuits are
+
+# a commonly studied class of quantum circuits comprised of commuting gates, for which we can
+
+# efficiently evaluate expectation values of tensor Pauli operators, but have become well known due to
+
+# a number of results related to the hardness of sampling their output distributions [1-3]. As a
+
+# consequence, IQP circuits are prime candidates for potential quantum advantages for tasks related to
+
+# sampling, and have been the focus of a number of theoretical [4-6] and experimental [7,8] works
+
+# aiming towards this goal.
+
+# 
+
+# In this demo we present the IQPopt package, which contains some tools that allow us to approximate
+
+# expectation values of PauliZ operators for these circuits and, therefore, to evaluate them in
+
+# generative optimization tasks.
+
+# 
+
+######################################################################
+# .. figure:: ../_static/demonstration_assets/iqpopt/iqpopt_main.png
+
+#    :alt: IQP circuit optimization
+
+# 
+
+#    IQP circuit optimization
+
+# 
+
+######################################################################
+# Creating a circuit
+
+# ------------------
+
+# 
+
+######################################################################
+# These are circuits comprised of gates :math:`\text{exp}(i\theta_j X_j)`, where the generator
+
+# :math:`X_j` is a tensor product of Pauli X operators acting on some subset of qubits and
+
+# :math:`\theta_j` is a trainable parameter. Input states and measurements are diagonal in the
+
+# computational (Z) basis.
+
+# 
+
+# To define such a circuit (with input state :math:`\vert 0 \rangle`) we need to specify the number of
+
+# qubits and the parameterized gates
+
+# 
+
+import iqpopt as iqp
+from iqpopt.utils import local_gates
+
+n_qubits = 3
+gates = local_gates(n_qubits, 2) # all gates with max weight 2
+
+circuit = iqp.IqpSimulator(n_qubits, gates)
+
+######################################################################
+# A set of gates is specified by a list
+
+# 
+
+# .. raw:: html
+
+# 
+
+#    <center>
+
+# 
+
+# gates = [gen1, gen2, gen3]
+
+# 
+
+# .. raw:: html
+
+# 
+
+#    </center>
+
+# 
+
+# that contains the generators of the gates. Since all gates have Pauli X type generators, a generator
+
+# is represented by a list that specifies which qubits are acted on by an X operator. For example,
+
+# 
+
+# .. raw:: html
+
+# 
+
+#    <center>
+
+# 
+
+# gen1 = [0,1]
+
+# 
+
+# .. raw:: html
+
+# 
+
+#    </center>
+
+# 
+
+# corresponds to a gate with generator :math:`X\otimes X` acting on the first two qubits of the
+
+# circuit. For a three qubit system the corresponding parameterized gate is therefore
+
+# :math:`\exp(i\theta_1 X\otimes X\otimes \mathbb{I})`.
+
+# 
+
+# The previous example prepares a three qubit circuit with all single and two qubit gates via the
+
+# function local_gates, i.e.
+
+# 
+
+# .. raw:: html
+
+# 
+
+#    <center>
+
+# 
+
+# gates = [[0], [1], [2], [0,1], [0,2], [1,2]]
+
+# 
+
+# .. raw:: html
+
+# 
+
+#    </center>
+
+# 
+
+######################################################################
+# Estimating expectation values
+
+# -----------------------------
+
+# 
+
+######################################################################
+# The class method ``IqpSimulator.op_expval()`` is used to provide estimates of expectation values.
+
+# The function requires a parameter array ``params``, a PauliZ operator specified by its binary
+
+# representation ``op``, the number of samples ``n_samples`` that controls the precision of the
+
+# approximation (the more the better), and a JAX pseudo random number generator key to seed the
+
+# randomness of the sampling.
+
+# 
+
+# Below is a simple example implementing this for the three qubit circuit defined above, which returns
+
+# the expectation value estimate as well as its standard error.
+
+# 
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+
+op = jnp.array([0,1,0]) # Pauli Z on the second qubit
+params = jnp.array(np.random.rand(len(circuit.gates)))
+n_samples = 1000
+key = jax.random.PRNGKey(42)
+
+expval, std = circuit.op_expval(params, op, n_samples, key)
+
+print(expval, std)
+
+######################################################################
+# The function also allows for fast batch evaluation of expectation values. If we specify a batch of
+
+# operators ``ops`` by an array we can batch evaluate the expectation values and errors in parallel
+
+# with the same syntax.
+
+# 
+
+ops = jnp.array([[1,0,0],[0,1,0],[0,0,1]]) # batch of single qubit Pauli Zs
+
+expvals, stds = circuit.op_expval(params, ops, n_samples, key)
+
+print(expvals, stds)
+
+######################################################################
+# Testing the expectation values with Pennylane
+
+# ---------------------------------------------
+
+# 
+
+######################################################################
+# When using new software, it is always good practice to check your results if possible. In our case
+
+# we have Pennylane, a well tested library that does this calculations for a small number of qubits.
+
+# On the `package's github <https://github.com/XanaduAI/iqpopt>`__ you can find already made functions
+
+# that calculate the same expectation values as ``.op_expval()``, taking the necessary info from an
+
+# ``IqpSimulator`` object.
+
+# 
+
+# Let's first download these functions so we don't have to build them again:
+
+# 
+
+import requests
+
+req = requests.get("https://raw.githubusercontent.com/XanaduAI/iqpopt/refs/heads/main/tests/pennylane_functions.py?token=GHSAT0AAAAAACYVZBT5V4YNS5U2YD6COCQYZ2YM32Q")
+
+with open("pennylane_functions.py", "wb") as f:
+    f.write(req.content)
+
+######################################################################
+# Using the same ``params`` and ``op`` as before (but using ``numpy`` arrays instead of
+
+# ``jax.numpy``), let's see the pennylane's result.
+
+# 
+
+from pennylane_functions import penn_op_expval
+
+penn_expval = penn_op_expval(circuit, np.array(params), np.array(op))
+
+print(penn_expval)
+
+######################################################################
+# Since the calculation on ``iqpopt``\ 's side is stochastic, the results are not exactly the same,
+
+# but as we can see, they are within std error. You can try increasing ``n_samples`` in order to
+
+# obtain closer approximations.
+
+# 
+
+######################################################################
+# Sampling and probabilities
+
+# --------------------------
+
+# 
+
+######################################################################
+# We can also view a parameterized IQP circuit as a generative model that generates samples of binary
+
+# vectors according to the distribution :raw-latex:`\begin{align}
+
+# q_{\boldsymbol{\theta}}(\boldsymbol{x}) \equiv q(\boldsymbol{x}\vert\boldsymbol{\theta})=\vert\bra{\boldsymbol{x}}U(\boldsymbol{\theta})\ket{0}\vert^2.
+
+# \end{align}` For low amounts of qubits, we can use Penylane's arsenal and know the output
+
+# probabilities of the circuit as well as sample from it. Note that there is not an efficient
+
+# algorithm to do these, so, for large numbers of qubits, these methods will either take too long or
+
+# not work at all.
+
+# 
+
+# These functions are already implemented in the ``IqpSimulator`` object, so we don't have to download
+
+# them.
+
+# 
+
+sample = circuit.sample(params, shots=1)
+print(sample)
+
+probabilities = circuit.probs(params)
+print(probabilities)
+
+######################################################################
+# The ``.probs()`` method works as it does in pennylane, the returned array of probabilities is in
+
+# lexicographic order.
+
+# 
+
+######################################################################
+# Optimizing a circuit
+
+# --------------------
+
+# 
+
+######################################################################
+# Circuits can be optimized via a separate ``Trainer`` class. To instantiate a trainer object we first
+
+# define a loss function (also called an objective function), an optimizer and an initial stepsize for
+
+# the gradient descent. Continuing our example from before, below we define a simple loss function
+
+# that is a sum of expectation values returned by ``.op_expval()`` .
+
+# 
+
+def loss_fn(params, circuit, ops, n_samples, key):
+    expvals = circuit.op_expval(params, ops, n_samples, key)[0]
+    return jnp.sum(expvals)
+
+optimizer = "Adam"
+stepsize = 0.001
+
+trainer = iqp.Trainer(optimizer, loss_fn, stepsize)
+
+######################################################################
+# Any differentiable loss function expressible in JAX can be defined, but must have a first argument
+
+# ``params`` that corresponds to the optimization parameters of the circuit. To minimize the loss
+
+# function, we call the method ``.train()`` , which requires the number of iterations ``n_iters`` and
+
+# the initial arguments of the loss function ``loss_kwargs`` given as a dictionary object. This
+
+# dictionary must contain a key ``params`` whose corresponding value specifies the initial parameters.
+
+# 
+
+import matplotlib.pyplot as plt
+
+n_iters = 1000
+params_init = np.random.normal(0, 1, len(circuit.gates))
+n_samples = 100
+
+loss_kwargs = {
+    "params": params_init,
+    "circuit": circuit,
+    "ops": ops,
+    "n_samples": n_samples,
+    "key": key,
+}
+
+trainer.train(n_iters, loss_kwargs)
+
+trained_params = trainer.final_params
+plt.plot(trainer.losses) # plot the loss curve
+plt.show()
+
+######################################################################
+# Generative machine learning tools
+
+# ---------------------------------
+
+# 
+
+# The package contains a module ``gen_qml`` with functionality to train and evaluate generative models
+
+# expressed as ``IqpSimulator`` circuits.
+
+# 
+
+######################################################################
+# Training via the maximum mean discrepancy loss
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+# 
+
+# The Maximum Mean Discrepancy (MMD) [9] is an integral probability metric that measures the
+
+# similarity between two probability distributions, and can serve as a loss function to train
+
+# generative models. It has one parameter called ``sigma``, the bandwidth of the used gaussian kernel.
+
+# 
+
+# This metric is usually calculated with samples from both probability distributions and we have an
+
+# implementation ready for this case in the ``gen_qml`` module.
+
+# 
+
+import iqpopt.gen_qml as gen
+from iqpopt.gen_qml.utils import median_heuristic
+
+n_bits = 100
+# toy datasets of low weight bitstrings
+X1 = np.random.binomial(1, 0.05, size=(100, n_bits))
+X2 = np.random.binomial(1, 0.07, size=(100, n_bits))
+
+sigma = median_heuristic(X1) # bandwidth for MMD
+
+mmd = gen.mmd_loss_samples(X1, X2, sigma)
+print(mmd)
+
+######################################################################
+# This metric can be determined only with expectation values of PauliZ operators, so, when instead of
+
+# samples, we have an ``IqpSimulator`` object, we can also calculate it comparing the circuit's
+
+# distribution with the ``ground_truth``.
+
+# 
+
+# The implementation in the ``gen_qml`` module only needs an additional parameter ``n_ops`` that
+
+# controls the accuracy of this value. For each of these ``n_ops``, an expectation value will be
+
+# calculated with ``n_samples``. The higher the number of operators the better.
+
+# 
+
+# Let's see it in an example with 20 qubits:
+
+# 
+
+n_qubits = 20
+
+# toy dataset of low weight bitstrings (from ground truth p)
+ground_truth = np.random.binomial(1, 0.2, size=(100, n_qubits))
+
+gates = local_gates(n_qubits, 2)
+circuit = iqp.IqpSimulator(n_qubits, gates)
+
+params = np.random.normal(0, 1/np.sqrt(n_qubits), len(gates))
+sigma = median_heuristic(ground_truth) # bandwidth for MMD
+
+mmd = gen.mmd_loss_iqp(params,
+                       circuit,
+                       ground_truth,
+                       sigma,
+                       n_ops=1000,
+                       n_samples=1000,
+                       key=jax.random.PRNGKey(42))
+print(mmd)
+
+######################################################################
+# Now, similar to what we did a few sections back in *Optimizing a circuit*, this function can be used
+
+# with a ``Trainer`` object to train a quantum generative model given as a parameterized IQP circuit.
+
+# 
+
+# Using the last defined 20 qubits circuit:
+
+# 
+
+X_train = np.random.binomial(1, 0.2, size=(1000, n_qubits))
+params_init = np.random.normal(0, 1/np.sqrt(n_qubits), len(gates))
+loss = gen.mmd_loss_iqp # MMD loss
+
+loss_kwargs = {
+    "params": params_init,
+    "iqp_circuit": circuit,
+    "ground_truth": X_train, # samples from ground truth distribution
+    "sigma": sigma,
+    "n_ops": 1000,
+    "n_samples": 1000,
+    "key": jax.random.PRNGKey(42),
+}
+
+trainer = iqp.Trainer("Adam", loss, stepsize=0.01)
+trainer.train(n_iters=250, loss_kwargs=loss_kwargs)
+
+trained_params = trainer.final_params
+plt.plot(trainer.losses)
+plt.show()
+
+######################################################################
+# Kernel Generalized Empirical Likelihood
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+# 
+
+# In order to probe how well a generative model captures a distribution of high-dimensional data, we
+
+# can use the Kernel Generalized Empirical Likelihood [10]. This metric is a measure of the closeness
+
+# of two distributions while also serving as a tool to evaluate mode dropping.
+
+# 
+
+# As before, if samples are available from the generative model, we have a straightforward
+
+# implementation of the KGEL test:
+
+# 
+
+n_qubits = 10
+
+# toy dataset of low weight bitstrings
+X = np.random.binomial(1, 0.2, size=(1000, n_qubits))
+
+n_witness = 10
+T = X[-n_witness:] # witness points (sampled from the ground truth distribution)
+X = X[:-n_witness] # ground truth for the kgel test
+
+# model samples
+Y = np.random.binomial(1, 0.3, size=(1000, n_qubits))
+
+sigma = median_heuristic(X) #bandwidth for KGEL
+kgel, p_kgel = gen.kgel_opt_samples(T, X, Y, sigma)
+print(kgel)
+
+######################################################################
+# To evaluate the KGEL for a model given by a ``IqpSimulator`` object, we need to use another
+
+# implementation based on the calculation of expectation values of PauliZ operators. Let's see how
+
+# well the last trained circuit matches the ground truth with the KGEL test:
+
+# 
+
+n_witness = 10
+T = X_train[-n_witness:] # witness points (sampled from the ground truth distribution)
+X = X_train[:-n_witness] # ground truth for the kgel test
+
+sigma = median_heuristic(X)
+kgel, p_kgel = gen.kgel_opt_iqp(circuit,
+                                trained_params,
+                                T,
+                                X,
+                                sigma,
+                                n_ops=1000,
+                                n_samples=1000,
+                                key=jax.random.PRNGKey(42))
+print(kgel)
+
+######################################################################
+# References:
+
+# 
+
+# [1] Michael J Bremner, Richard Jozsa, and Dan J Shepherd. "Classical simulation of commuting
+
+# quantum computations implies collapse of the polynomial hierarchy". In: Proceedings of the Royal
+
+# Society A: Mathematical, Physical and Engineering Sciences 467.2126 (2011), pp. 459-472 (page 1).
+
+# 
+
+# [2] Michael J Bremner, Ashley Montanaro, and Dan J Shepherd. "Average-case complexity versus
+
+# approximate simulation of commuting quantum computations". In: Physical review letters 117.8 (2016),
+
+# p. 080501 (page 1).
+
+# 
+
+# [3] Simon C Marshall, Scott Aaronson, and Vedran Dunjko. "Improved separation between quantum and
+
+# classical computers for sampling and functional tasks". In: arXiv preprint arXiv:2410.20935 (2024)
+
+# (page 1).
+
+# 
+
+# [4] Michael J Bremner, Ashley Montanaro, and Dan J Shepherd. "Achieving quantum supremacy with
+
+# sparse and noisy commuting quantum computations". In: Quantum 1 (2017), p. 8 (page 1).
+
+# 
+
+# [5] Tomoyuki Morimae and Suguru Tamaki. "Additive-error fine-grained quantum supremacy". In: Quantum
+
+# 4 (2020), p. 329 (page 1).
+
+# 
+
+# [6] Louis Paletta, Anthony Leverrier, Alain Sarlette, Mazyar Mirrahimi, and Christophe Vuillot.
+
+# "Robust sparse IQP sampling in constant depth". In: Quantum 8 (2024), p. 1337 (page 1).
+
+# 
+
+# [7] Dolev Bluvstein, Simon J Evered, Alexandra A Geim, Sophie H Li, Hengyun Zhou, Tom Manovitz,
+
+# Sepehr Ebadi, Madelyn Cain, Marcin Kalinowski, Dominik Hangleiter, et al. "Logical quantum processor
+
+# based on reconfigurable atom arrays". In: Nature 626.7997 (2024), pp. 58-65 (pages 1, 2).
+
+# 
+
+# [8] Dominik Hangleiter, Marcin Kalinowski, Dolev Bluvstein, Madelyn Cain, Nishad Maskara, Xun Gao,
+
+# Aleksander Kubica, Mikhail D Lukin, and Michael J Gullans. "Fault-tolerant compiling of classically
+
+# hard IQP circuits on hypercubes". In: arXiv preprint arXiv:2404.19005 (2024) (page 1).
+
+# 
+
+# [9] Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola.
+
+# "A Kernel Two-Sample Test". In: Journal of Machine Learning Research 13.25 (2012), pp. 723-773. url:
+
+# http://jmlr.org/papers/v13/gretton12a.html (page 14).
+
+# 
+
+# [10] Suman Ravuri, Mélanie Rey, Shakir Mohamed, and Marc Deisenroth. Understanding Deep Generative
+
+# Models with Generalized Empirical Likelihoods. 2023. arXiv: 2306.09780 [cs.LG]. url:
+
+# https://arxiv.org/abs/2306.09780 (pages 3, 16).
+
+# 
+
+######################################################################
+# About the author
+# ----------------
+# # .. include:: ../_static/authors/erik_recio.txt

From 88b7164611a860ded6e53a1b1cbb26470341313e Mon Sep 17 00:00:00 2001
From: Erik Recio <erikrecio@gmail.com>
Date: Thu, 12 Dec 2024 16:51:42 +0100
Subject: [PATCH 2/7] 2nd version of the iqpopt tutorial

---
 .../thumbnail_iqpopt.png                      | Bin 0 -> 253431 bytes
 .../iqpopt/sample_time.png                    | Bin 0 -> 82518 bytes
 demonstrations/tutorial_iqpopt.metadata.json  |  32 ++
 demonstrations/tutorial_iqpopt.py             | 489 ++++++++++++------
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 create mode 100644 _static/demo_thumbnails/regular_demo_thumbnails/thumbnail_iqpopt.png
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diff --git a/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_iqpopt.png b/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_iqpopt.png
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diff --git a/demonstrations/tutorial_iqpopt.metadata.json b/demonstrations/tutorial_iqpopt.metadata.json
new file mode 100644
index 0000000000..2afab7a270
--- /dev/null
+++ b/demonstrations/tutorial_iqpopt.metadata.json
@@ -0,0 +1,32 @@
+{
+    "title": "IQPopt: Fast optimization of IQP circuits in JAX",
+    "authors": [
+        {
+            "username": "erik.recio"
+        }
+    ],
+    "dateOfPublication": "2024-12-12T00:00:00+00:00",
+    "dateOfLastModification": "2024-12-12T00:00:00+00:00",
+    "categories": [
+        "Optimization"
+    ],
+    "tags": [],
+    "previewImages": [
+        {
+            "type": "thumbnail",
+            "uri": "/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_iqpopt.png"
+        }
+    ],
+    "seoDescription": "Showing how to use the package IQPopt and optimize IQP circuits really fast.",
+    "doi": "",
+    "references": [],
+    "basedOnPapers": [],
+    "referencedByPapers": [],
+    "relatedContent": [
+        {
+            "type": "tutorial",
+            "id": "tutorial_iqpopt_package",
+            "weight": 1.0
+        }
+    ]
+}
diff --git a/demonstrations/tutorial_iqpopt.py b/demonstrations/tutorial_iqpopt.py
index 4d7db84349..3d8a9ca532 100644
--- a/demonstrations/tutorial_iqpopt.py
+++ b/demonstrations/tutorial_iqpopt.py
@@ -53,9 +53,9 @@
 # 
 
 ######################################################################
-# Creating a circuit
+# Creating an IQP circuit with pennylane
 
-# ------------------
+# --------------------------------------
 
 # 
 
@@ -70,22 +70,9 @@
 
 # 
 
-# To define such a circuit (with input state :math:`\vert 0 \rangle`) we need to specify the number of
+# To define such a circuit, we need a way to represent the parameterized gates. A set of gates will be
 
-# qubits and the parameterized gates
-
-# 
-
-import iqpopt as iqp
-from iqpopt.utils import local_gates
-
-n_qubits = 3
-gates = local_gates(n_qubits, 2) # all gates with max weight 2
-
-circuit = iqp.IqpSimulator(n_qubits, gates)
-
-######################################################################
-# A set of gates is specified by a list
+# specified by a list
 
 # 
 
@@ -109,9 +96,11 @@
 
 # 
 
-# that contains the generators of the gates. Since all gates have Pauli X type generators, a generator
+# that contains the generators of the gates (each generator with an independent trainable parameter).
 
-# is represented by a list that specifies which qubits are acted on by an X operator. For example,
+# Since all gates have Pauli X type generators, a generator will be represented by a list that
+
+# specifies which qubits are acted on by an X operator. For example,
 
 # 
 
@@ -143,9 +132,7 @@
 
 # 
 
-# The previous example prepares a three qubit circuit with all single and two qubit gates via the
-
-# function local_gates, i.e.
+# Here is how to prepare a three qubit circuit with all the possible single and two qubit gates
 
 # 
 
@@ -169,113 +156,343 @@
 
 # 
 
-######################################################################
-# Estimating expectation values
+# In order to be more general and have more than one generator with the same trainable parameter, we
 
-# -----------------------------
+# are going to group the generators with a tuple. Therefore, if the first and second generators share
+
+# the same parameter, then
 
 # 
 
-######################################################################
-# The class method ``IqpSimulator.op_expval()`` is used to provide estimates of expectation values.
+# .. raw:: html
 
-# The function requires a parameter array ``params``, a PauliZ operator specified by its binary
+# 
 
-# representation ``op``, the number of samples ``n_samples`` that controls the precision of the
+#    <center>
 
-# approximation (the more the better), and a JAX pseudo random number generator key to seed the
+# 
 
-# randomness of the sampling.
+# gates = [([0], [1]), ([2],), ([0,1],), ([0,2],), ([1,2],)]
 
 # 
 
-# Below is a simple example implementing this for the three qubit circuit defined above, which returns
+# .. raw:: html
 
-# the expectation value estimate as well as its standard error.
+# 
+
+#    </center>
 
 # 
 
-import jax
-import jax.numpy as jnp
+# As we can see, the rest of the generators are alone in their own tuple. This would be the final form
+
+# of our gates parameter, which will define our circuit from now on.
+
+# 
+
+# Let's now put it in practice and build a pennylane circuit out of this ``gates`` parameter. The
+
+# circuit will be build thanks to the following identity:
+
+# 
+
+# .. math:: \text{exp}(i\theta_j X_j) = H^{\otimes n} \text{exp}(i\theta_j Z_j) H^{\otimes n}
+
+# 
+
+# where :math:`H` is the Hadamard matrix and :math:`Z_j` is the same tensor product of Paulis, but now
+
+# instead of Pauli X there are Pauli Z with the corresponding identities.
+
+# 
+
+# Our pennyalne circuit (with input state :math:`\vert 0 \rangle`) would therefore be the following.
+
+# 
+
+import pennylane as qml
 import numpy as np
 
-op = jnp.array([0,1,0]) # Pauli Z on the second qubit
-params = jnp.array(np.random.rand(len(circuit.gates)))
-n_samples = 1000
-key = jax.random.PRNGKey(42)
+def penn_iqp_gates(params: np.ndarray, gates: list, n_qubits: int):
+    """IQP circuit in pennylane form.
 
-expval, std = circuit.op_expval(params, op, n_samples, key)
+    Args:
+        params (np.ndarray): The parameters of the IQP gates.
+        gates (list): The gates representation of the IQP circuit.
+        n_qubits (int): The total number of qubits in the circuit.
+    """
 
-print(expval, std)
+    for i in range(n_qubits):
+        qml.Hadamard(i)
+
+    for par, gate in zip(params, gates):
+        for gen in gate:
+            qml.MultiRZ(2*par, wires=gen)
+
+    for i in range(n_qubits):
+        qml.Hadamard(i)
 
 ######################################################################
-# The function also allows for fast batch evaluation of expectation values. If we specify a batch of
+# Now, we want to measure our circuit. As we said, all measurements are diagonal in the computational
 
-# operators ``ops`` by an array we can batch evaluate the expectation values and errors in parallel
+# (Z) basis, and we will therefore represent them with a bitstring. 1s, wherever there are Pauli Z,
 
-# with the same syntax.
+# and 0s, wherever there are identities. In this way, an operator such as:
 
 # 
 
-ops = jnp.array([[1,0,0],[0,1,0],[0,0,1]]) # batch of single qubit Pauli Zs
+# .. math:: O = Z \otimes I \otimes Z
 
-expvals, stds = circuit.op_expval(params, ops, n_samples, key)
+# 
 
-print(expvals, stds)
+# will be converted to
+
+# 
+
+# .. raw:: html
+
+# 
+
+#    <center>
+
+# 
+
+# op = [1,0,1]
+
+# 
+
+# .. raw:: html
+
+# 
+
+#    </center>
+
+# 
+
+# The final circuit that allow us to measure these type of expectation values is the following.
+
+# 
+
+def penn_obs(op: np.ndarray) -> qml.operation.Observable:
+    """Returns a pennylane observable from a bitstring representation.
+
+    Args:
+        op (np.ndarray): Bitstring representation of the Z operator.
+
+    Returns:
+        qml.Observable: Pennylane observable.
+    """
+    for i, z in enumerate(op):
+        if i==0:
+            if z:
+                obs = qml.Z(i)
+            else:
+                obs = qml.I(i)
+        else:
+            if z:
+                obs @= qml.Z(i)
+    return obs
+
+def penn_iqp_circuit(params: np.ndarray, gates: list, op: np.ndarray, n_qubits: int) -> qml.measurements.ExpectationMP:
+    """Defines the circuit that calculates the expectation value of the operator with the IQP circuit with pennylane tools.
+
+    Args:
+        params (np.ndarray): The parameters of the IQP gates.
+        gates (list): The gates representation of the IQP circuit.
+        op (np.ndarray): Bitstring representation of the Z operator.
+        n_qubits (int): The total number of qubits in the circuit.
+
+    Returns:
+        qml.measurements.ExpectationMP: Pennylane circuit with an expectation value.
+    """
+    penn_iqp_gates(params, gates, n_qubits)
+    obs = penn_obs(op)
+    return qml.expval(obs)
+
+def penn_iqp_op_expval(params: np.ndarray, gates: list, op: np.ndarray, n_qubits: int) -> float:
+    """Calculates the expectation value of the operator with the IQP circuit with pennylane tools.
+
+    Args:
+        params (np.ndarray): The parameters of the IQP gates.
+        gates (list): The gates representation of the IQP circuit.
+        op (np.ndarray): Bitstring representation of the Z operator.
+        n_qubits (int): The total number of qubits in the circuit.
+
+    Returns:
+        float: Expectation value.
+    """
+    dev = qml.device("lightning.qubit", wires=n_qubits)
+    penn_iqp_circuit_exe = qml.QNode(penn_iqp_circuit, dev)
+    return penn_iqp_circuit_exe(params, gates, op, n_qubits)
 
 ######################################################################
-# Testing the expectation values with Pennylane
+# With this, we can now calculate all expectation of Pauli Z tensors of any IQP circuit we can think
 
-# ---------------------------------------------
+# of. Let's see an example.
 
 # 
 
+n_qubits = 3
+gates = [([0], [1]), ([2],), ([0,1],), ([0,2],), ([1,2],)] # same gates as in the example above
+op = np.array([1,0,1]) # operator ZIZ
+params = np.random.rand(len(gates)) # random parameters for all the gates (remember we have 5 gates with 6 generators in total)
+
+penn_op_expval = penn_iqp_op_expval(params, gates, op, n_qubits)
+print(penn_op_expval)
+
 ######################################################################
-# When using new software, it is always good practice to check your results if possible. In our case
+# Estimating expectation values with IQPopt
+
+# -----------------------------------------
+
+# 
+
+# With this new package we can perform the same calculations our pennylane circuit above is able to
+
+# do. Although, this time, only with approximations instead of exact values. The gain is that we can
 
-# we have Pennylane, a well tested library that does this calculations for a small number of qubits.
+# work with a very large amount of qubits.
 
-# On the `package's github <https://github.com/XanaduAI/iqpopt>`__ you can find already made functions
+# 
 
-# that calculate the same expectation values as ``.op_expval()``, taking the necessary info from an
+# After some calculations, one can arrive at the following expression when calculating the expectation
 
-# ``IqpSimulator`` object.
+# value of any tensor product of Pauli Z operators
 
 # 
 
-# Let's first download these functions so we don't have to build them again:
+# .. math:: \langle Z_{\boldsymbol{a}} \rangle = \mathbb{E}_{\boldsymbol{z}\sim U}\Big[ \cos\Big(\sum_j \theta_{j}(-1)^{\boldsymbol{g}_{j}\cdot \boldsymbol{z}}(1-(-1)^{\boldsymbol{g}_j\cdot \boldsymbol{a}}\Big) \Big],
 
 # 
 
-import requests
+# where :math:`\boldsymbol{a}` is the bitstring form of the operator we want to calculate the
+
+# expectation value of, :math:`\boldsymbol{z}` are bitstring samples from the multi-qubit system taken
+
+# from the uniform distribution, :math:`\theta_{j}` are the trainable parameters and
+
+# :math:`\boldsymbol{g}_{j}` are the different generators also in bitstring form. The dots are dot
+
+# products between the bitstrings taken as vectors of 1s and 0s.
+
+# 
 
-req = requests.get("https://raw.githubusercontent.com/XanaduAI/iqpopt/refs/heads/main/tests/pennylane_functions.py?token=GHSAT0AAAAAACYVZBT5V4YNS5U2YD6COCQYZ2YM32Q")
+# We could classically calculate this expression exactly, but it would require an exponentially large
+
+# amount of :math:`\boldsymbol{z}` samples with the increasing number of qubits. To aliviate this, we
+
+# can replace the expectation with an empirical mean and compute an unbiased estimate of
+
+# :math:`\langle Z_{\boldsymbol{a}} \rangle` efficiently. That is, if we sample a batch of :math:`s`
+
+# bit strings :math:`\{\boldsymbol{z}_i\}` from the uniform distribution and compute the sample mean
+
+# 
 
-with open("pennylane_functions.py", "wb") as f:
-    f.write(req.content)
+# .. math:: \hat{\langle Z_{\boldsymbol{a}}\rangle} = \frac{1}{s}\sum_{i}\cos\Big(\sum_j \theta_j(-1)^{\boldsymbol{g}_j\cdot \boldsymbol{z}_i}(1-(-1)^{\boldsymbol{g}_j\cdot \boldsymbol{a}})\Big),
+
+# 
+
+# we obtain an unbiased estimate :math:`\hat{\langle Z_{\boldsymbol{a}}\rangle}` of
+
+# :math:`\langle Z_{\boldsymbol{a}}\rangle`; i.e. we have that
+
+# :math:`\mathbb{E}[\hat{\langle Z_{\boldsymbol{a}}\rangle}] = \langle Z_{\boldsymbol{a}}\rangle`. The
+
+# error of this approximation is well known since, by the central limit theorem, the standard
+
+# deviation of the sample mean of a bounded random variable decreases as
+
+# :math:`\mathcal{O}(1/\sqrt{s})`.
+
+# 
+
+# Let's see now how to use the IQPopt package to calculate expectation values, using the same
+
+# arguments we used in the previous example. First, we create the circuit object with ``IqpSimulator``
+
+# only with the number of qubits of the circuit and the ``gates`` parameter already explained:
+
+# 
+
+import iqpopt as iqp
+
+small_circuit = iqp.IqpSimulator(n_qubits, gates)
 
 ######################################################################
-# Using the same ``params`` and ``op`` as before (but using ``numpy`` arrays instead of
+# Now, we use the class method ``IqpSimulator.op_expval()``, which is the one that provides these
+
+# estimates. This function requires a parameter array ``params``, a PauliZ operator specified by its
+
+# binary representation ``op``, a new parameter ``n_samples`` (the number of samples :math:`s`) that
+
+# controls the precision of the approximation (the more the better), and a JAX pseudo random number
 
-# ``jax.numpy``), let's see the pennylane's result.
+# generator key to seed the randomness of the sampling. It returns the expectation value estimate as
+
+# well as its standard error.
 
 # 
 
-from pennylane_functions import penn_op_expval
+# Using the same ``params`` and ``op`` as before:
+
+# 
 
-penn_expval = penn_op_expval(circuit, np.array(params), np.array(op))
+import jax
 
-print(penn_expval)
+n_samples = 1000
+key = jax.random.PRNGKey(42)
+
+expval, std = small_circuit.op_expval(params, op, n_samples, key)
+
+print(expval, std)
 
 ######################################################################
-# Since the calculation on ``iqpopt``\ 's side is stochastic, the results are not exactly the same,
+# Since the calculation on ``iqpopt``\ 's side is stochastic, the result is not exactly the same than
 
-# but as we can see, they are within std error. You can try increasing ``n_samples`` in order to
+# the one obtained with pennylane. But, as we can see, they are within std error. You can try
 
-# obtain closer approximations.
+# increasing ``n_samples`` in order to obtain closer approximations.
 
 # 
 
+# This function also allows for fast batch evaluation of expectation values. If we specify a batch of
+
+# operators ``ops`` by an array, we can batch evaluate the expectation values and errors in parallel
+
+# with the same syntax.
+
+# 
+
+ops = np.array([[1,0,0],[0,1,0],[0,0,1]]) # batch of single qubit Pauli Zs
+
+expvals, stds = small_circuit.op_expval(params, ops, n_samples, key)
+
+print(expvals, stds)
+
+######################################################################
+# With pennylane it would be very time consuming to pass the 30 qubit mark, but with IQPopt, we can
+
+# easily go way further than that.
+
+# 
+
+from iqpopt.utils import local_gates
+
+n_qubits = 1000
+gates = local_gates(n_qubits, 1) # 1000 single qubit generators with independent trainable parameters
+
+large_circuit = iqp.IqpSimulator(n_qubits, gates)
+
+params = np.random.rand(len(gates))
+op = np.random.randint(0, 2, n_qubits)
+n_samples = 1000
+key = jax.random.PRNGKey(42)
+
+expval, std = large_circuit.op_expval(params, op, n_samples, key)
+
+print(expval, std)
+
 ######################################################################
 # Sampling and probabilities
 
@@ -286,43 +503,66 @@
 ######################################################################
 # We can also view a parameterized IQP circuit as a generative model that generates samples of binary
 
-# vectors according to the distribution :raw-latex:`\begin{align}
+# vectors according to the distribution
 
-# q_{\boldsymbol{\theta}}(\boldsymbol{x}) \equiv q(\boldsymbol{x}\vert\boldsymbol{\theta})=\vert\bra{\boldsymbol{x}}U(\boldsymbol{\theta})\ket{0}\vert^2.
+# 
 
-# \end{align}` For low amounts of qubits, we can use Penylane's arsenal and know the output
+# .. math:: q_{\boldsymbol{\theta}}(\boldsymbol{x}) \equiv q(\boldsymbol{x}\vert\boldsymbol{\theta})=\vert\bra{\boldsymbol{x}}U(\boldsymbol{\theta})\ket{0}\vert^2.
 
-# probabilities of the circuit as well as sample from it. Note that there is not an efficient
+# 
 
-# algorithm to do these, so, for large numbers of qubits, these methods will either take too long or
+# For a low amount of qubits, we can use Pennylane's arsenal and know the output probabilities of the
 
-# not work at all.
+# circuit as well as sample from it. Note that there is not an efficient algorithm to do these, so,
+
+# for large numbers of qubits, these methods will either take too long or not work at all.
 
 # 
 
-# These functions are already implemented in the ``IqpSimulator`` object, so we don't have to download
+# These functions are already implemented in the ``IqpSimulator`` object. The ``.probs()`` method
 
-# them.
+# works as it does in pennylane, the returned array of probabilities is in lexicographic order.
 
 # 
 
-sample = circuit.sample(params, shots=1)
+sample = small_circuit.sample(params, shots=1)
 print(sample)
 
-probabilities = circuit.probs(params)
+probabilities = small_circuit.probs(params)
 print(probabilities)
 
+sample = large_circuit.sample(params, shots=1)
+print(sample)
+
+probabilities = large_circuit.probs(params)
+print(probabilities)
+
+######################################################################
+# As we can see, we can't sample or know the probabilities of the circuit for the large one. The only
+
+# efficient approximation algorithm we have is for the calculation of expectation values. In the
+
+# following figure, you can see how the time scales with the different methods when we increase the
+
+# number of qubits. The calculation of expectation values is the only one that remains linear instead
+
+# of increasing exponentially.
+
+# 
+
 ######################################################################
-# The ``.probs()`` method works as it does in pennylane, the returned array of probabilities is in
+# .. figure:: ../_static/demonstration_assets/iqpopt/sample_time.png
 
-# lexicographic order.
+#    :alt: Time vs n_qubits
 
 # 
 
+#    Time vs n_qubits
+
 ######################################################################
-# Optimizing a circuit
+# Optimizing an IQPopt circuit
 
-# --------------------
+# ----------------------------
 
 # 
 
@@ -331,12 +571,14 @@
 
 # define a loss function (also called an objective function), an optimizer and an initial stepsize for
 
-# the gradient descent. Continuing our example from before, below we define a simple loss function
+# the gradient descent. Continuing our ``small_circuit`` example from before, below we define a simple
 
-# that is a sum of expectation values returned by ``.op_expval()`` .
+# loss function that is a sum of expectation values returned by ``.op_expval()`` .
 
 # 
 
+import jax.numpy as jnp
+
 def loss_fn(params, circuit, ops, n_samples, key):
     expvals = circuit.op_expval(params, ops, n_samples, key)[0]
     return jnp.sum(expvals)
@@ -362,12 +604,12 @@ def loss_fn(params, circuit, ops, n_samples, key):
 import matplotlib.pyplot as plt
 
 n_iters = 1000
-params_init = np.random.normal(0, 1, len(circuit.gates))
+params_init = np.random.normal(0, 1, len(small_circuit.gates))
 n_samples = 100
 
 loss_kwargs = {
     "params": params_init,
-    "circuit": circuit,
+    "circuit": small_circuit,
     "ops": ops,
     "n_samples": n_samples,
     "key": key,
@@ -499,81 +741,20 @@ def loss_fn(params, circuit, ops, n_samples, key):
 plt.plot(trainer.losses)
 plt.show()
 
-######################################################################
-# Kernel Generalized Empirical Likelihood
-
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-# 
-
-# In order to probe how well a generative model captures a distribution of high-dimensional data, we
-
-# can use the Kernel Generalized Empirical Likelihood [10]. This metric is a measure of the closeness
-
-# of two distributions while also serving as a tool to evaluate mode dropping.
-
-# 
-
-# As before, if samples are available from the generative model, we have a straightforward
-
-# implementation of the KGEL test:
-
-# 
-
-n_qubits = 10
-
-# toy dataset of low weight bitstrings
-X = np.random.binomial(1, 0.2, size=(1000, n_qubits))
-
-n_witness = 10
-T = X[-n_witness:] # witness points (sampled from the ground truth distribution)
-X = X[:-n_witness] # ground truth for the kgel test
-
-# model samples
-Y = np.random.binomial(1, 0.3, size=(1000, n_qubits))
-
-sigma = median_heuristic(X) #bandwidth for KGEL
-kgel, p_kgel = gen.kgel_opt_samples(T, X, Y, sigma)
-print(kgel)
-
-######################################################################
-# To evaluate the KGEL for a model given by a ``IqpSimulator`` object, we need to use another
-
-# implementation based on the calculation of expectation values of PauliZ operators. Let's see how
-
-# well the last trained circuit matches the ground truth with the KGEL test:
-
-# 
-
-n_witness = 10
-T = X_train[-n_witness:] # witness points (sampled from the ground truth distribution)
-X = X_train[:-n_witness] # ground truth for the kgel test
-
-sigma = median_heuristic(X)
-kgel, p_kgel = gen.kgel_opt_iqp(circuit,
-                                trained_params,
-                                T,
-                                X,
-                                sigma,
-                                n_ops=1000,
-                                n_samples=1000,
-                                key=jax.random.PRNGKey(42))
-print(kgel)
-
 ######################################################################
 # References:
 
 # 
 
-# [1] Michael J Bremner, Richard Jozsa, and Dan J Shepherd. "Classical simulation of commuting
+# [1] Michael J Bremner, Richard Jozsa, and Dan J Shepherd. "Classical simulation of com- muting
 
-# quantum computations implies collapse of the polynomial hierarchy". In: Proceedings of the Royal
+# quantum computations implies collapse of the polynomial hierarchy". In: Proceed- ings of the Royal
 
 # Society A: Mathematical, Physical and Engineering Sciences 467.2126 (2011), pp. 459-472 (page 1).
 
 # 
 
-# [2] Michael J Bremner, Ashley Montanaro, and Dan J Shepherd. "Average-case complexity versus
+# [2] Michael J Bremner, Ashley Montanaro, and Dan J Shepherd. "Average-case complex- ity versus
 
 # approximate simulation of commuting quantum computations". In: Physical review letters 117.8 (2016),
 
@@ -623,7 +804,7 @@ def loss_fn(params, circuit, ops, n_samples, key):
 
 # 
 
-# [9] Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola.
+# [9] Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexan- der Smola.
 
 # "A Kernel Two-Sample Test". In: Journal of Machine Learning Research 13.25 (2012), pp. 723-773. url:
 
@@ -631,14 +812,6 @@ def loss_fn(params, circuit, ops, n_samples, key):
 
 # 
 
-# [10] Suman Ravuri, Mélanie Rey, Shakir Mohamed, and Marc Deisenroth. Understanding Deep Generative
-
-# Models with Generalized Empirical Likelihoods. 2023. arXiv: 2306.09780 [cs.LG]. url:
-
-# https://arxiv.org/abs/2306.09780 (pages 3, 16).
-
-# 
-
 ######################################################################
 # About the author
 # ----------------

From eb603ee8bd25abe9803e7b084e536c36fc117741 Mon Sep 17 00:00:00 2001
From: Erik Recio <erikrecio@gmail.com>
Date: Thu, 12 Dec 2024 17:03:19 +0100
Subject: [PATCH 3/7] metadata update

---
 demonstrations/tutorial_iqpopt.metadata.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demonstrations/tutorial_iqpopt.metadata.json b/demonstrations/tutorial_iqpopt.metadata.json
index 2afab7a270..9787db2439 100644
--- a/demonstrations/tutorial_iqpopt.metadata.json
+++ b/demonstrations/tutorial_iqpopt.metadata.json
@@ -24,7 +24,7 @@
     "referencedByPapers": [],
     "relatedContent": [
         {
-            "type": "tutorial",
+            "type": "demonstration",
             "id": "tutorial_iqpopt_package",
             "weight": 1.0
         }

From 249c6a6b4de1844d7d8494532e76f8b9bc3c2f8f Mon Sep 17 00:00:00 2001
From: Erik <erikrecio@gmail.com>
Date: Thu, 16 Jan 2025 19:06:14 +0100
Subject: [PATCH 4/7] update

---
 Makefile                                     |   1 +
 _static/authors/erik_recio.jpg               | Bin 417476 -> 1024748 bytes
 demonstrations/tutorial_iqpopt.metadata.json |   2 +-
 demonstrations/tutorial_iqpopt.py            | 451 +++++++++++--------
 pyproject.toml                               |   1 +
 5 files changed, 264 insertions(+), 191 deletions(-)

diff --git a/Makefile b/Makefile
index ff5c334968..5ae4651475 100644
--- a/Makefile
+++ b/Makefile
@@ -79,5 +79,6 @@ environment:
 			$$PYTHON_VENV_PATH/bin/python -m pip install --extra-index-url https://test.pypi.org/simple/ PennyLane-Catalyst --pre --upgrade;\
 			$$PYTHON_VENV_PATH/bin/python -m pip install --extra-index-url https://test.pypi.org/simple/ PennyLane-Lightning --pre --upgrade;\
 			$$PYTHON_VENV_PATH/bin/python -m pip install --upgrade git+https://github.com/PennyLaneAI/pennylane.git#egg=pennylane;\
+			$$PYTHON_VENV_PATH/bin/python -m pip install git+https://github.com/XanaduAI/iqpopt.git#egg=iqpopt;\
 		fi;\
 	fi
diff --git a/_static/authors/erik_recio.jpg b/_static/authors/erik_recio.jpg
index 779a3e2b3608a323a6cd5e534e33a4b8b06ac650..884ab80a5cc710510a06f581ca900ee55c4c7c8e 100644
GIT binary patch
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diff --git a/demonstrations/tutorial_iqpopt.metadata.json b/demonstrations/tutorial_iqpopt.metadata.json
index 9787db2439..b94ecbd237 100644
--- a/demonstrations/tutorial_iqpopt.metadata.json
+++ b/demonstrations/tutorial_iqpopt.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2024-12-12T00:00:00+00:00",
-    "dateOfLastModification": "2024-12-12T00:00:00+00:00",
+    "dateOfLastModification": "2025-01-16T00:00:00+00:00",
     "categories": [
         "Optimization"
     ],
diff --git a/demonstrations/tutorial_iqpopt.py b/demonstrations/tutorial_iqpopt.py
index 3d8a9ca532..0c63b49347 100644
--- a/demonstrations/tutorial_iqpopt.py
+++ b/demonstrations/tutorial_iqpopt.py
@@ -5,74 +5,74 @@
 """
 
 ######################################################################
-# The Pennylane library offers the possibility of simulating any type of quantum circuit, but the
+# Instantaneous Quantum Polynomial (IQP) circuits are a class of circuits that are expected to be hard
 
-# resources needed always scale exponentially with the number of qubits. There are some classes of
+# to sample from using classical computers [2]. In this demo we take a look at the pacakge IQPopt [1],
 
-# quantum citcuits that we know can be efficiently simulated, and this work attempts to fulfil part of
-
-# this niche.
+# which shows that despite this, such circuits can still be optimized efficiently!
 
 # 
 
-# IQPopt is a package designed for fast optimization of parameterized Instantaneous Quantum Polynomial
-
-# (IQP) circuits using JAX, resulting in a linear scaling on the number of qubits. These circuits are
+# As we will see, this hinges on a suprising fact about these circuits: while sampling is hard,
 
-# a commonly studied class of quantum circuits comprised of commuting gates, for which we can
+# estimating expectation values of certain observables is easy.
 
-# efficiently evaluate expectation values of tensor Pauli operators, but have become well known due to
+# 
 
-# a number of results related to the hardness of sampling their output distributions [1-3]. As a
+######################################################################
+# .. figure:: iqpopt_main.png
 
-# consequence, IQP circuits are prime candidates for potential quantum advantages for tasks related to
+#    :alt: IQP circuit optimization
 
-# sampling, and have been the focus of a number of theoretical [4-6] and experimental [7,8] works
+# 
 
-# aiming towards this goal.
+#    IQP circuit optimization
 
 # 
 
-# In this demo we present the IQPopt package, which contains some tools that allow us to approximate
-
-# expectation values of PauliZ operators for these circuits and, therefore, to evaluate them in
+######################################################################
+# Parameterized IQP circuits
 
-# generative optimization tasks.
+# ==========================
 
 # 
 
 ######################################################################
-# .. figure:: ../_static/demonstration_assets/iqpopt/iqpopt_main.png
+# IQP is designed to optimize a class of IQP circuits called *parameterized IQP circuits*. These are
 
-#    :alt: IQP circuit optimization
+# comprised of gates :math:`\text{exp}(i\theta_j X_j)`, where the generator :math:`X_j` is a tensor
 
-# 
+# product of Pauli X operators acting on some subset of qubits and :math:`\theta_j` is a trainable
 
-#    IQP circuit optimization
+# parameter. We will represent the parameterized gates by a list
 
 # 
 
-######################################################################
-# Creating an IQP circuit with pennylane
+# .. raw:: html
 
-# --------------------------------------
+# 
+
+#    <center>
 
 # 
 
-######################################################################
-# These are circuits comprised of gates :math:`\text{exp}(i\theta_j X_j)`, where the generator
+# gates = [gen1, gen2, ...]
 
-# :math:`X_j` is a tensor product of Pauli X operators acting on some subset of qubits and
+# 
 
-# :math:`\theta_j` is a trainable parameter. Input states and measurements are diagonal in the
+# .. raw:: html
 
-# computational (Z) basis.
+# 
+
+#    </center>
 
 # 
 
-# To define such a circuit, we need a way to represent the parameterized gates. A set of gates will be
+# that specifies the generators of the gates (each generator with an independent trainable parameter).
 
-# specified by a list
+# Each element of ``gates`` corresponds to a different parameter, and is a list of lists of integers
+
+# that specifiy the generators for that parameter. For example,
 
 # 
 
@@ -84,7 +84,7 @@
 
 # 
 
-# gates = [gen1, gen2, gen3]
+# gen1 = [[0,1]]
 
 # 
 
@@ -96,11 +96,9 @@
 
 # 
 
-# that contains the generators of the gates (each generator with an independent trainable parameter).
-
-# Since all gates have Pauli X type generators, a generator will be represented by a list that
+# specifies that the first gate has one generator which corresponds to :math:`X\otimes X` acting on
 
-# specifies which qubits are acted on by an X operator. For example,
+# the first two qubits of the circuit. A generator
 
 # 
 
@@ -112,7 +110,7 @@
 
 # 
 
-# gen1 = [0,1]
+# gen1 = [[0,1], [0]]
 
 # 
 
@@ -124,43 +122,48 @@
 
 # 
 
-# corresponds to a gate with generator :math:`X\otimes X` acting on the first two qubits of the
+# would correspond to a gate with a single parameter with generator :math:`X\otimes X` +
 
-# circuit. For a three qubit system the corresponding parameterized gate is therefore
-
-# :math:`\exp(i\theta_1 X\otimes X\otimes \mathbb{I})`.
+# :math:`X\otimes I`.
 
 # 
 
-# Here is how to prepare a three qubit circuit with all the possible single and two qubit gates
+# Here we will work with the following set of gates
 
 # 
 
-# .. raw:: html
+gates = [[[0]], [[1]], [[2]], [[0,1]], [[0,2]], [[1,2]]]
 
-# 
-
-#    <center>
+######################################################################
+# i.e., all one and two body generators acting on three qubits.
 
 # 
 
-# gates = [[0], [1], [2], [0,1], [0,2], [1,2]]
+######################################################################
+# Expectation values
+
+# ------------------
 
 # 
 
-# .. raw:: html
+######################################################################
+# IQPopt can be applied to problems that involve measuring expectation values of Pauli Z tensors of
+
+# parameterized IQP circuits.
 
 # 
 
-#    </center>
+# We will represent these observables with binary lists, where a non-zero element specifies that
+
+# presence of a Pauli Z operator on that qubit. For example, for a three qubit circuit the operator
 
 # 
 
-# In order to be more general and have more than one generator with the same trainable parameter, we
+# .. math:: O = Z \otimes I \otimes Z
 
-# are going to group the generators with a tuple. Therefore, if the first and second generators share
+# 
 
-# the same parameter, then
+# will be represented as
 
 # 
 
@@ -172,7 +175,7 @@
 
 # 
 
-# gates = [([0], [1]), ([2],), ([0,1],), ([0,2],), ([1,2],)]
+# op = [1,0,1]
 
 # 
 
@@ -184,15 +187,21 @@
 
 # 
 
-# As we can see, the rest of the generators are alone in their own tuple. This would be the final form
+# Let's now put this into practice and build a pennylane circuit out of a ``gates`` list.
+
+# 
+
+######################################################################
+# Creating an IQP circuit with pennylane
 
-# of our gates parameter, which will define our circuit from now on.
+# --------------------------------------
 
 # 
 
-# Let's now put it in practice and build a pennylane circuit out of this ``gates`` parameter. The
+######################################################################
+# To build a parameterized IQP circuit in PennyLane, we can use the MultiRZ function, making use of
 
-# circuit will be build thanks to the following identity:
+# the identity
 
 # 
 
@@ -200,13 +209,11 @@
 
 # 
 
-# where :math:`H` is the Hadamard matrix and :math:`Z_j` is the same tensor product of Paulis, but now
+# where :math:`H` is the Hadamard matrix and :math:`Z_j` is the operator obtained by replacing the
 
-# instead of Pauli X there are Pauli Z with the corresponding identities.
+# Pauli X operators by Pauli Z operators in :math:`X_j`. Our pennyalne circuit (with input state
 
-# 
-
-# Our pennyalne circuit (with input state :math:`\vert 0 \rangle`) would therefore be the following.
+# :math:`\vert 0 \rangle`) is therefore the following.
 
 # 
 
@@ -233,43 +240,9 @@ def penn_iqp_gates(params: np.ndarray, gates: list, n_qubits: int):
         qml.Hadamard(i)
 
 ######################################################################
-# Now, we want to measure our circuit. As we said, all measurements are diagonal in the computational
-
-# (Z) basis, and we will therefore represent them with a bitstring. 1s, wherever there are Pauli Z,
-
-# and 0s, wherever there are identities. In this way, an operator such as:
+# Now we have our circuit, we can evaluate expectation values of tensor products of Pauli Z operators
 
-# 
-
-# .. math:: O = Z \otimes I \otimes Z
-
-# 
-
-# will be converted to
-
-# 
-
-# .. raw:: html
-
-# 
-
-#    <center>
-
-# 
-
-# op = [1,0,1]
-
-# 
-
-# .. raw:: html
-
-# 
-
-#    </center>
-
-# 
-
-# The final circuit that allow us to measure these type of expectation values is the following.
+# specified by lists of the form ``op`` above.
 
 # 
 
@@ -326,14 +299,13 @@ def penn_iqp_op_expval(params: np.ndarray, gates: list, op: np.ndarray, n_qubits
     return penn_iqp_circuit_exe(params, gates, op, n_qubits)
 
 ######################################################################
-# With this, we can now calculate all expectation of Pauli Z tensors of any IQP circuit we can think
+# With this, we can now calculate all expectation values of Pauli Z tensors of any parameterizec IQP
 
-# of. Let's see an example.
+# circuit we can think of. Let's see an example using our ``gates`` list from above.
 
 # 
 
 n_qubits = 3
-gates = [([0], [1]), ([2],), ([0,1],), ([0,2],), ([1,2],)] # same gates as in the example above
 op = np.array([1,0,1]) # operator ZIZ
 params = np.random.rand(len(gates)) # random parameters for all the gates (remember we have 5 gates with 6 generators in total)
 
@@ -347,17 +319,17 @@ def penn_iqp_op_expval(params: np.ndarray, gates: list, op: np.ndarray, n_qubits
 
 # 
 
-# With this new package we can perform the same calculations our pennylane circuit above is able to
+# IQPopt is able to perform the same operations our pennylane circuit above is able to do. Although,
 
-# do. Although, this time, only with approximations instead of exact values. The gain is that we can
+# this time, only with approximations instead of exact values. The gain is that we can work with very
 
-# work with a very large amount of qubits.
+# large circuits.
 
 # 
 
-# After some calculations, one can arrive at the following expression when calculating the expectation
+# Starting from a paper [5] from Van den Nest (Theorem 3), one can arrive at the following expression
 
-# value of any tensor product of Pauli Z operators
+# for expectation values of Pauli Z operators
 
 # 
 
@@ -367,25 +339,19 @@ def penn_iqp_op_expval(params: np.ndarray, gates: list, op: np.ndarray, n_qubits
 
 # where :math:`\boldsymbol{a}` is the bitstring form of the operator we want to calculate the
 
-# expectation value of, :math:`\boldsymbol{z}` are bitstring samples from the multi-qubit system taken
-
-# from the uniform distribution, :math:`\theta_{j}` are the trainable parameters and
+# expectation value of, :math:`\boldsymbol{z}` are bitstring samples taken from the uniform
 
-# :math:`\boldsymbol{g}_{j}` are the different generators also in bitstring form. The dots are dot
+# distribution, :math:`\theta_{j}` are the trainable parameters and :math:`\boldsymbol{g}_{j}` are the
 
-# products between the bitstrings taken as vectors of 1s and 0s.
-
-# 
+# different generators also in bitstring form. Although this expression is exact, computing the
 
-# We could classically calculate this expression exactly, but it would require an exponentially large
+# expectation exactly requires an infite number of samples :math:`\boldsymbol{z}`. Instead, we can
 
-# amount of :math:`\boldsymbol{z}` samples with the increasing number of qubits. To aliviate this, we
-
-# can replace the expectation with an empirical mean and compute an unbiased estimate of
+# replace the expectation with an empirical mean and compute an unbiased estimate of
 
 # :math:`\langle Z_{\boldsymbol{a}} \rangle` efficiently. That is, if we sample a batch of :math:`s`
 
-# bit strings :math:`\{\boldsymbol{z}_i\}` from the uniform distribution and compute the sample mean
+# bitstrings :math:`\{\boldsymbol{z}_i\}` from the uniform distribution and compute the sample mean
 
 # 
 
@@ -407,7 +373,7 @@ def penn_iqp_op_expval(params: np.ndarray, gates: list, op: np.ndarray, n_qubits
 
 # 
 
-# Let's see now how to use the IQPopt package to calculate expectation values, using the same
+# Lets see now how to use the IQPopt package to calculate expectation values, using the same
 
 # arguments we used in the previous example. First, we create the circuit object with ``IqpSimulator``
 
@@ -420,17 +386,17 @@ def penn_iqp_op_expval(params: np.ndarray, gates: list, op: np.ndarray, n_qubits
 small_circuit = iqp.IqpSimulator(n_qubits, gates)
 
 ######################################################################
-# Now, we use the class method ``IqpSimulator.op_expval()``, which is the one that provides these
+# To obtain estimates of expectation values we use the class method ``IqpSimulator.op_expval()``. This
 
-# estimates. This function requires a parameter array ``params``, a PauliZ operator specified by its
+# function requires a parameter array ``params``, a PauliZ operator specified by its binary
 
-# binary representation ``op``, a new parameter ``n_samples`` (the number of samples :math:`s`) that
+# representation ``op``, a new parameter ``n_samples`` (the number of samples :math:`s`) that controls
 
-# controls the precision of the approximation (the more the better), and a JAX pseudo random number
+# the precision of the approximation (the more the better), and a JAX pseudo random number generator
 
-# generator key to seed the randomness of the sampling. It returns the expectation value estimate as
+# key to seed the randomness of the sampling. It returns the expectation value estimate as well as its
 
-# well as its standard error.
+# standard error.
 
 # 
 
@@ -440,8 +406,8 @@ def penn_iqp_op_expval(params: np.ndarray, gates: list, op: np.ndarray, n_qubits
 
 import jax
 
-n_samples = 1000
-key = jax.random.PRNGKey(42)
+n_samples = 2000
+key = jax.random.PRNGKey(66)
 
 expval, std = small_circuit.op_expval(params, op, n_samples, key)
 
@@ -477,10 +443,15 @@ def penn_iqp_op_expval(params: np.ndarray, gates: list, op: np.ndarray, n_qubits
 
 # 
 
-from iqpopt.utils import local_gates
-
 n_qubits = 1000
-gates = local_gates(n_qubits, 1) # 1000 single qubit generators with independent trainable parameters
+n_gates = 1000
+
+gates = []
+for _ in range(n_gates):
+    # First we create a generator with bodyness 2
+    gen = list(np.random.choice(n_qubits, 2, replace=False))
+    # Each gen will have its independent trainable parameter, so we can directly build the gate
+    gates.append([gen])
 
 large_circuit = iqp.IqpSimulator(n_qubits, gates)
 
@@ -501,9 +472,9 @@ def penn_iqp_op_expval(params: np.ndarray, gates: list, op: np.ndarray, n_qubits
 # 
 
 ######################################################################
-# We can also view a parameterized IQP circuit as a generative model that generates samples of binary
+# If we measure the output qubits of an IQP circuit we generate samples of binary vectors according to
 
-# vectors according to the distribution
+# the distribution
 
 # 
 
@@ -511,11 +482,13 @@ def penn_iqp_op_expval(params: np.ndarray, gates: list, op: np.ndarray, n_qubits
 
 # 
 
-# For a low amount of qubits, we can use Pennylane's arsenal and know the output probabilities of the
+# Where :math:`U(\boldsymbol{\theta})` is the parametrized IQP gates. For a low number of qubits, we
+
+# can use Pennylane to obtain the output probabilities of the circuit as well as sample from it. Note
 
-# circuit as well as sample from it. Note that there is not an efficient algorithm to do these, so,
+# that there is not an efficient algorithm to do these so for large numbers of qubits so PennyLane
 
-# for large numbers of qubits, these methods will either take too long or not work at all.
+# returns an error in this case.
 
 # 
 
@@ -540,24 +513,66 @@ def penn_iqp_op_expval(params: np.ndarray, gates: list, op: np.ndarray, n_qubits
 ######################################################################
 # As we can see, we can't sample or know the probabilities of the circuit for the large one. The only
 
-# efficient approximation algorithm we have is for the calculation of expectation values. In the
+# efficient approximation algorithm we have is for the calculation of expectation values. Let's see
 
-# following figure, you can see how the time scales with the different methods when we increase the
+# how time scales for each of the methods.
 
-# number of qubits. The calculation of expectation values is the only one that remains linear instead
+# 
 
-# of increasing exponentially.
+import time
+import matplotlib.pyplot as plt
 
-# 
+range_qubits = range(15, 26)
+n_gates = 10
+n_samples = 1000
+
+times_op, times_sample, times_probs = [], [], []
+for n_qubits in range_qubits:
+    
+    gates = []
+    for _ in range(n_gates):
+        gen = list(np.random.choice(n_qubits, 2, replace=False))
+        gates.append([gen])
+    
+    circuit = iqp.IqpSimulator(n_qubits, gates)
+    params_init = np.random.uniform(0, 2*np.pi, len(gates))
+    key = jax.random.PRNGKey(np.random.randint(0, 99999))
+    op = np.random.randint(0, 2, n_qubits)
+    
+    # Timing op_expval
+    start = time.perf_counter()
+    circuit.op_expval(params_init, op, n_samples, key)
+    times_op.append(time.perf_counter() - start)
+    
+    # Timing sample
+    start = time.perf_counter()
+    circuit.sample(params_init, shots=1)
+    times_sample.append(time.perf_counter() - start)
+    
+    # Timing probs
+    start = time.perf_counter()
+    circuit.probs(params_init)
+    times_probs.append(time.perf_counter() - start)
+
+plt.scatter(range_qubits[1:], times_op[1:], label="op_expval")
+plt.scatter(range_qubits[1:], times_sample[1:], label="sample")
+plt.scatter(range_qubits[1:], times_probs[1:], label="probs")
+
+plt.xlabel("n_qubits")
+plt.ylabel("Time [s]")
+plt.yscale("log")
+plt.title(f"Time vs n_qubits")
+plt.legend()
+plt.show()
 
 ######################################################################
-# .. figure:: ../_static/demonstration_assets/iqpopt/sample_time.png
+# In the previous figure, you can see that the time to sample or compute probabilities scales
 
-#    :alt: Time vs n_qubits
+# exponentially, however expectation values are very efficient (the scaling can be shown to be
 
-# 
+# linear).
 
-#    Time vs n_qubits
+# 
 
 ######################################################################
 # Optimizing an IQPopt circuit
@@ -601,7 +616,7 @@ def loss_fn(params, circuit, ops, n_samples, key):
 
 # 
 
-import matplotlib.pyplot as plt
+np.random.seed(0)
 
 n_iters = 1000
 params_init = np.random.normal(0, 1, len(small_circuit.gates))
@@ -612,15 +627,22 @@ def loss_fn(params, circuit, ops, n_samples, key):
     "circuit": small_circuit,
     "ops": ops,
     "n_samples": n_samples,
-    "key": key,
+    "key": key
 }
 
-trainer.train(n_iters, loss_kwargs)
+trainer.train(n_iters, loss_kwargs, turbo=100) # the turbo option trains in iteration batches of the number that you input, using jit and lax.scan
 
 trained_params = trainer.final_params
 plt.plot(trainer.losses) # plot the loss curve
 plt.show()
 
+######################################################################
+# This training process finds its global minimum at loss = -3.0, which is the minimum possible with
+
+# the defined loss function.
+
+# 
+
 ######################################################################
 # Generative machine learning tools
 
@@ -628,9 +650,13 @@ def loss_fn(params, circuit, ops, n_samples, key):
 
 # 
 
-# The package contains a module ``gen_qml`` with functionality to train and evaluate generative models
+# The package contains a dedicated module ``gen_qml`` with functionality to train and evaluate
+
+# generative models expressed as ``IqpSimulator`` circuits. Note that since sampling from IQP circuits
+
+# is hard, these circuits may lead to advantages for generative machine learning tasks relative to
 
-# expressed as ``IqpSimulator`` circuits.
+# classical models!
 
 # 
 
@@ -641,21 +667,27 @@ def loss_fn(params, circuit, ops, n_samples, key):
 
 # 
 
-# The Maximum Mean Discrepancy (MMD) [9] is an integral probability metric that measures the
+# The Maximum Mean Discrepancy (MMD) [6] is an integral probability metric that measures the
 
 # similarity between two probability distributions, and can serve as a loss function to train
 
-# generative models. It has one parameter called ``sigma``, the bandwidth of the used gaussian kernel.
+# generative models.
 
 # 
 
-# This metric is usually calculated with samples from both probability distributions and we have an
+# .. math::  \text{MMD}^2(\boldsymbol{\theta}) = \mathbb{E}_{\boldsymbol{x},\boldsymbol{y}\sim q_{\boldsymbol{\theta}} }[k(\boldsymbol{x},\boldsymbol{y})] - 2  \mathbb{E}_{\boldsymbol{x} \sim q_{\boldsymbol{\theta}},\boldsymbol{y}\sim p }[k(\boldsymbol{x},\boldsymbol{y})] + \mathbb{E}_{\boldsymbol{x},\boldsymbol{y}\sim p }[k(\boldsymbol{x},\boldsymbol{y})] \,,
+
+# 
+
+# Using a Gaussian kernel, it has one parameter called ``sigma``, the bandwidth of this kernel. This
+
+# metric is usually calculated with samples from both probability distributions and we have an
 
 # implementation ready for this case in the ``gen_qml`` module.
 
 # 
 
-import iqpopt.gen_qml as gen
+import iqpopt.gen_qml as genq
 from iqpopt.gen_qml.utils import median_heuristic
 
 n_bits = 100
@@ -665,23 +697,23 @@ def loss_fn(params, circuit, ops, n_samples, key):
 
 sigma = median_heuristic(X1) # bandwidth for MMD
 
-mmd = gen.mmd_loss_samples(X1, X2, sigma)
+mmd = genq.mmd_loss_samples(X1, X2, sigma)
 print(mmd)
 
 ######################################################################
-# This metric can be determined only with expectation values of PauliZ operators, so, when instead of
-
-# samples, we have an ``IqpSimulator`` object, we can also calculate it comparing the circuit's
+# This metric can also be estimated efficiently with expectation values of PauliZ operators only [7].
 
-# distribution with the ``ground_truth``.
+# This means that if we have an ``IqpSimulator`` object, we can also estiamte the MMD loss.
 
 # 
 
-# The implementation in the ``gen_qml`` module only needs an additional parameter ``n_ops`` that
+# The implementation in the ``gen_qml`` module only needs an additional parameter, ``n_ops``, that
 
 # controls the accuracy of this value. For each of these ``n_ops``, an expectation value will be
 
-# calculated with ``n_samples``. The higher the number of operators the better.
+# calculated with ``n_samples``. The higher the number of operators and samples, the better the
+
+# precision.
 
 # 
 
@@ -690,17 +722,24 @@ def loss_fn(params, circuit, ops, n_samples, key):
 # 
 
 n_qubits = 20
+n_gates = 200
 
 # toy dataset of low weight bitstrings (from ground truth p)
 ground_truth = np.random.binomial(1, 0.2, size=(100, n_qubits))
 
-gates = local_gates(n_qubits, 2)
+gates = []
+for _ in range(n_gates):
+    gen = list(np.random.choice(n_qubits, 2, replace=False)) # generators with bodyness 2
+    gates.append([gen])
+    
 circuit = iqp.IqpSimulator(n_qubits, gates)
 
 params = np.random.normal(0, 1/np.sqrt(n_qubits), len(gates))
-sigma = median_heuristic(ground_truth) # bandwidth for MMD
+sigma = median_heuristic(ground_truth)/3 # bandwidth for MMD
+
+print(sigma)
 
-mmd = gen.mmd_loss_iqp(params,
+mmd = genq.mmd_loss_iqp(params,
                        circuit,
                        ground_truth,
                        sigma,
@@ -722,7 +761,7 @@ def loss_fn(params, circuit, ops, n_samples, key):
 
 X_train = np.random.binomial(1, 0.2, size=(1000, n_qubits))
 params_init = np.random.normal(0, 1/np.sqrt(n_qubits), len(gates))
-loss = gen.mmd_loss_iqp # MMD loss
+loss = genq.mmd_loss_iqp # MMD loss
 
 loss_kwargs = {
     "params": params_init,
@@ -735,60 +774,82 @@ def loss_fn(params, circuit, ops, n_samples, key):
 }
 
 trainer = iqp.Trainer("Adam", loss, stepsize=0.01)
-trainer.train(n_iters=250, loss_kwargs=loss_kwargs)
+trainer.train(n_iters=300, loss_kwargs=loss_kwargs, turbo=10) 
 
 trained_params = trainer.final_params
 plt.plot(trainer.losses)
 plt.show()
 
 ######################################################################
-# References:
-
-# 
+# We can now try to see how well this generative IQP circuit resembles the ground truth. Since we are
 
-# [1] Michael J Bremner, Richard Jozsa, and Dan J Shepherd. "Classical simulation of com- muting
+# not working with a large number of qubits, we can sample from the circuit with the pennylane
 
-# quantum computations implies collapse of the polynomial hierarchy". In: Proceed- ings of the Royal
+# machinery. We can then compare our trained and untrained samples with the ground truth through a
 
-# Society A: Mathematical, Physical and Engineering Sciences 467.2126 (2011), pp. 459-472 (page 1).
+# histogram of the bitstring weights and evaluate the distributions.
 
 # 
 
-# [2] Michael J Bremner, Ashley Montanaro, and Dan J Shepherd. "Average-case complex- ity versus
+samples_untrained = circuit.sample(params_init, 1000)
+plt.hist(np.sum(samples_untrained, axis=1), bins=20, range=[0,20])
+plt.title("Histogram of the untrained circuit")
+plt.show()
 
-# approximate simulation of commuting quantum computations". In: Physical review letters 117.8 (2016),
+samples_trained = circuit.sample(trainer.final_params, 1000)
+plt.hist(np.sum(samples_trained, axis=1), bins=20, range=[0,20])
+plt.title("Histogram of the trained circuit")
+plt.show()
 
-# p. 080501 (page 1).
+plt.hist(np.sum(X_train, axis=1), bins=20, range=[0,20])
+plt.title("Histogram of the ground truth")
+plt.show()
+
+######################################################################
+# As we can see the trained circuit closely resembles the ground truth distribution. Although we won't
+
+# cover it in this demo, the pacakge also contains tools to evaluate generative models and invtesigate
+
+# model dropping via the Kernel Generalized Empirical Likelikood.
 
 # 
 
-# [3] Simon C Marshall, Scott Aaronson, and Vedran Dunjko. "Improved separation between quantum and
+# As a final takeaway, we remark that IQPopt is perhaps the only tool for researchers analyze large
+
+# circuits with potential advantages using numerical methods. By doing so, it opens up opportunities
 
-# classical computers for sampling and functional tasks". In: arXiv preprint arXiv:2410.20935 (2024)
+# to explore systems that are too intricate for traditional pen-and-paper calculations and, as a
 
-# (page 1).
+# result, it has the potential to uncover insights that were previously inaccessible.
 
 # 
 
-# [4] Michael J Bremner, Ashley Montanaro, and Dan J Shepherd. "Achieving quantum supremacy with
+######################################################################
+# References:
 
-# sparse and noisy commuting quantum computations". In: Quantum 1 (2017), p. 8 (page 1).
+# 
+
+# [1] Erik Recio, Joseph Bowles. "IQPopt: Fast optimization of instantaneous quantum polynomial
+
+# circuits in JAX".
 
 # 
 
-# [5] Tomoyuki Morimae and Suguru Tamaki. "Additive-error fine-grained quantum supremacy". In: Quantum
+# [2] Michael J Bremner, Richard Jozsa, and Dan J Shepherd. "Classical simulation of commuting quantum
+
+# computations implies collapse of the polynomial hierarchy". In: Proceedings of the Royal Society A:
 
-# 4 (2020), p. 329 (page 1).
+# Mathematical, Physical and Engineering Sciences 467.2126 (2011), pp. 459-472 (page 1).
 
 # 
 
-# [6] Louis Paletta, Anthony Leverrier, Alain Sarlette, Mazyar Mirrahimi, and Christophe Vuillot.
+# [3] Michael J Bremner, Ashley Montanaro, and Dan J Shepherd. "Achieving quantum supremacy with
 
-# "Robust sparse IQP sampling in constant depth". In: Quantum 8 (2024), p. 1337 (page 1).
+# sparse and noisy commuting quantum computations". In: Quantum 1 (2017), p. 8 (page 1).
 
 # 
 
-# [7] Dolev Bluvstein, Simon J Evered, Alexandra A Geim, Sophie H Li, Hengyun Zhou, Tom Manovitz,
+# [4] Dolev Bluvstein, Simon J Evered, Alexandra A Geim, Sophie H Li, Hengyun Zhou, Tom Manovitz,
 
 # Sepehr Ebadi, Madelyn Cain, Marcin Kalinowski, Dominik Hangleiter, et al. "Logical quantum processor
 
@@ -796,15 +857,13 @@ def loss_fn(params, circuit, ops, n_samples, key):
 
 # 
 
-# [8] Dominik Hangleiter, Marcin Kalinowski, Dolev Bluvstein, Madelyn Cain, Nishad Maskara, Xun Gao,
-
-# Aleksander Kubica, Mikhail D Lukin, and Michael J Gullans. "Fault-tolerant compiling of classically
+# [5] M. Van den Nest. "Simulating quantum computers with probabilistic methods". 2010. arXiv:
 
-# hard IQP circuits on hypercubes". In: arXiv preprint arXiv:2404.19005 (2024) (page 1).
+# 0911.1624 [quant-ph]. url: https://arxiv.org/abs/0911.1624 (pages 2, 5).
 
 # 
 
-# [9] Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexan- der Smola.
+# [6] Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola.
 
 # "A Kernel Two-Sample Test". In: Journal of Machine Learning Research 13.25 (2012), pp. 723-773. url:
 
@@ -812,6 +871,18 @@ def loss_fn(params, circuit, ops, n_samples, key):
 
 # 
 
+# [7] Erik Recio, Shahnawaz Ahmed, Joseph Bowles. "How to train a 1000 qubit quantum generative model
+
+# on a laptop", in preparation
+
+# 
+
+# [8] Suman Ravuri, Mélanie Rey, Shakir Mohamed, Marc Peter Deisenroth, "Understanding Deep Generative
+
+# Models with Generalized Empirical Likelihoods"
+
+# 
+
 ######################################################################
 # About the author
 # ----------------
diff --git a/pyproject.toml b/pyproject.toml
index 9bed5d9256..6761c0fe6f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -79,6 +79,7 @@ qiskit = ">=1.0.0"
 qiskit-aer = ">=0.14.0"
 sphinxcontrib-applehelp = "1.0.8"
 sphinx-gallery = "0.17.1"
+iqpopt = "2024.7.0"
 
 
 # Install a difference version of torch from PyPI as the one from PyTorch repo is not compatible with MacOS

From 290adfc68e2b6d9da853a9fb69110d366848230c Mon Sep 17 00:00:00 2001
From: Erik <erikrecio@gmail.com>
Date: Thu, 16 Jan 2025 19:22:12 +0100
Subject: [PATCH 5/7] pypro

---
 pyproject.toml | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 6761c0fe6f..b5a0dc86e8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,11 +38,11 @@ pypandoc = "1.5"
 # These pinned versions of PL are NOT used for Dev builds #
 #      The latest commit from GitHub is used instead      #
 ###########################################################
-pennylane = "0.39.0"
-pennylane-cirq = "0.39.0"
-pennylane-qiskit = "0.39.0"
-pennylane-qulacs = "0.39.0"
-pennylane-catalyst = "0.9.0"
+pennylane = "0.40.0"
+pennylane-cirq = "0.40.0"
+pennylane-qiskit = "0.40.0"
+pennylane-qulacs = "0.40.0"
+pennylane-catalyst = "0.10.0"
 ##########################################################
 
 
@@ -65,12 +65,12 @@ optax = "0.2.3"
 flax = "0.9.0"
 qutip = "4.7.3"
 mitiq = "0.32.0"
-pennylane-qrack = "0.10.6"
+pennylane-qrack = "0.11.1"
 pyqrack = "1.32.12"
 zstd = "*"
 dill = "*"
 stim = "*"
-bluequbit = "0.8.1b1"
+bluequbit = "0.9.3b1"
 quimb = "1.8.2"
 aiohttp = "3.9.5"
 fsspec = "2024.6.1"

From 5caf236b70ef12e71d727a0787d6ff9af856954c Mon Sep 17 00:00:00 2001
From: Erik <erikrecio@gmail.com>
Date: Mon, 20 Jan 2025 18:24:13 +0100
Subject: [PATCH 6/7] poetry lock

---
 Makefile                                      |    2 +-
 .../demo_checker}/demo_diffs.md               |    0
 poetry.lock                                   | 4648 ++++++++++-------
 pyproject.toml                                |    2 +-
 4 files changed, 2727 insertions(+), 1925 deletions(-)
 rename {demo_checker => demonstrations/demo_checker}/demo_diffs.md (100%)

diff --git a/Makefile b/Makefile
index 5ae4651475..6a6fd6cc85 100644
--- a/Makefile
+++ b/Makefile
@@ -79,6 +79,6 @@ environment:
 			$$PYTHON_VENV_PATH/bin/python -m pip install --extra-index-url https://test.pypi.org/simple/ PennyLane-Catalyst --pre --upgrade;\
 			$$PYTHON_VENV_PATH/bin/python -m pip install --extra-index-url https://test.pypi.org/simple/ PennyLane-Lightning --pre --upgrade;\
 			$$PYTHON_VENV_PATH/bin/python -m pip install --upgrade git+https://github.com/PennyLaneAI/pennylane.git#egg=pennylane;\
-			$$PYTHON_VENV_PATH/bin/python -m pip install git+https://github.com/XanaduAI/iqpopt.git#egg=iqpopt;\
+			$$PYTHON_VENV_PATH/bin/python -m pip install --upgrade git+https://github.com/XanaduAI/iqpopt.git#egg=iqpopt;\
 		fi;\
 	fi
diff --git a/demo_checker/demo_diffs.md b/demonstrations/demo_checker/demo_diffs.md
similarity index 100%
rename from demo_checker/demo_diffs.md
rename to demonstrations/demo_checker/demo_diffs.md
diff --git a/poetry.lock b/poetry.lock
index 1a93345c53..a984a310f1 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -6,6 +6,7 @@ version = "2.1.0"
 description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff"},
     {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
@@ -17,6 +18,7 @@ version = "24.1.0"
 description = "File support for asyncio."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"},
     {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"},
@@ -28,6 +30,7 @@ version = "3.9.5"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fcde4c397f673fdec23e6b05ebf8d4751314fa7c24f93334bf1f1364c1c69ac7"},
     {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5d6b3f1fabe465e819aed2c421a6743d8debbde79b6a8600739300630a01bf2c"},
@@ -120,13 +123,14 @@ speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
-version = "1.3.1"
+version = "1.3.2"
 description = "aiosignal: a list of registered asynchronous callbacks"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
-    {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"},
-    {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"},
+    {file = "aiosignal-1.3.2-py2.py3-none-any.whl", hash = "sha256:45cde58e409a301715980c2b01d0c28bdde3770d8290b5eb2173759d9acb31a5"},
+    {file = "aiosignal-1.3.2.tar.gz", hash = "sha256:a8c255c66fafb1e499c9351d0bf32ff2d8a0321595ebac3b93713656d2436f54"},
 ]
 
 [package.dependencies]
@@ -138,6 +142,7 @@ version = "0.7.16"
 description = "A light, configurable Sphinx theme"
 optional = false
 python-versions = ">=3.9"
+groups = ["base", "executable-dependencies"]
 files = [
     {file = "alabaster-0.7.16-py3-none-any.whl", hash = "sha256:b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92"},
     {file = "alabaster-0.7.16.tar.gz", hash = "sha256:75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65"},
@@ -145,13 +150,14 @@ files = [
 
 [[package]]
 name = "alembic"
-version = "1.13.3"
+version = "1.14.1"
 description = "A database migration tool for SQLAlchemy."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
-    {file = "alembic-1.13.3-py3-none-any.whl", hash = "sha256:908e905976d15235fae59c9ac42c4c5b75cfcefe3d27c0fbf7ae15a37715d80e"},
-    {file = "alembic-1.13.3.tar.gz", hash = "sha256:203503117415561e203aa14541740643a611f641517f0209fcae63e9fa09f1a2"},
+    {file = "alembic-1.14.1-py3-none-any.whl", hash = "sha256:1acdd7a3a478e208b0503cd73614d5e4c6efafa4e73518bb60e4f2846a37b1c5"},
+    {file = "alembic-1.14.1.tar.gz", hash = "sha256:496e888245a53adf1498fcab31713a469c65836f8de76e01399aa1c3e90dd213"},
 ]
 
 [package.dependencies]
@@ -160,7 +166,7 @@ SQLAlchemy = ">=1.3.0"
 typing-extensions = ">=4"
 
 [package.extras]
-tz = ["backports.zoneinfo"]
+tz = ["backports.zoneinfo", "tzdata"]
 
 [[package]]
 name = "annotated-types"
@@ -168,6 +174,7 @@ version = "0.7.0"
 description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
@@ -175,24 +182,25 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.6.0"
+version = "4.8.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
-    {file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"},
-    {file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"},
+    {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
+    {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
 ]
 
 [package.dependencies]
 exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -201,6 +209,7 @@ version = "1.4.4"
 description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
     {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
     {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
@@ -212,6 +221,7 @@ version = "1.3.0"
 description = "Better dates & times for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["metadata-validation"]
 files = [
     {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"},
     {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"},
@@ -227,21 +237,19 @@ test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock
 
 [[package]]
 name = "asttokens"
-version = "2.4.1"
+version = "3.0.0"
 description = "Annotate AST trees with source code positions"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
-    {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
-    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
+    {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"},
+    {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"},
 ]
 
-[package.dependencies]
-six = ">=1.12.0"
-
 [package.extras]
-astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
-test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+astroid = ["astroid (>=2,<4)"]
+test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "astunparse"
@@ -249,6 +257,7 @@ version = "1.6.3"
 description = "An AST unparser for Python"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
     {file = "astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"},
     {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"},
@@ -264,6 +273,7 @@ version = "4.0.3"
 description = "Timeout context manager for asyncio programs"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
     {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"},
@@ -271,32 +281,34 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "24.2.0"
+version = "24.3.0"
 description = "Classes Without Boilerplate"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
+groups = ["executable-dependencies", "metadata-validation"]
 files = [
-    {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
-    {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
+    {file = "attrs-24.3.0-py3-none-any.whl", hash = "sha256:ac96cd038792094f438ad1f6ff80837353805ac950cd2aa0e0625ef19850c308"},
+    {file = "attrs-24.3.0.tar.gz", hash = "sha256:8f5c07333d543103541ba7be0e2ce16eeee8130cb0b3f9238ab904ce1e85baff"},
 ]
 
 [package.extras]
 benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "authlib"
-version = "1.3.2"
+version = "1.4.0"
 description = "The ultimate Python library in building OAuth and OpenID Connect servers and clients."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
-    {file = "Authlib-1.3.2-py2.py3-none-any.whl", hash = "sha256:ede026a95e9f5cdc2d4364a52103f5405e75aa156357e831ef2bfd0bc5094dfc"},
-    {file = "authlib-1.3.2.tar.gz", hash = "sha256:4b16130117f9eb82aa6eec97f6dd4673c3f960ac0283ccdae2897ee4bc030ba2"},
+    {file = "Authlib-1.4.0-py2.py3-none-any.whl", hash = "sha256:4bb20b978c8b636222b549317c1815e1fe62234fc1c5efe8855d84aebf3a74e3"},
+    {file = "authlib-1.4.0.tar.gz", hash = "sha256:1c1e6608b5ed3624aeeee136ca7f8c120d6f51f731aa152b153d54741840e1f2"},
 ]
 
 [package.dependencies]
@@ -308,6 +320,7 @@ version = "1.7.0"
 description = "Efficiently computes derivatives of NumPy code."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "autograd-1.7.0-py3-none-any.whl", hash = "sha256:49680300f842f3a8722b060ac0d3ed7aca071d1ad4d3d38c9fdadafdcc73c30b"},
     {file = "autograd-1.7.0.tar.gz", hash = "sha256:de743fd368d6df523cd37305dcd171861a9752a144493677d2c9f5a56983ff2f"},
@@ -321,13 +334,14 @@ scipy = ["scipy"]
 
 [[package]]
 name = "autoray"
-version = "0.6.12"
+version = "0.7.0"
 description = "Abstract your array operations."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
-    {file = "autoray-0.6.12-py3-none-any.whl", hash = "sha256:3ed7a4abcec052bcbb4f0447c426d0a0b9b9fa03ab71e76eaa77747ca43ac3e2"},
-    {file = "autoray-0.6.12.tar.gz", hash = "sha256:721328aa06fc3577155d988052614a7b4bd6e4d01b340695344031ee4abd2a1e"},
+    {file = "autoray-0.7.0-py3-none-any.whl", hash = "sha256:03103957df3d1b66b8068158056c2909a72095b19d1b24262261276a714a5d07"},
+    {file = "autoray-0.7.0.tar.gz", hash = "sha256:7829d21258512f87e02f23ce74ae5759af4ce8998069d2cce53468f1d701a219"},
 ]
 
 [package.extras]
@@ -340,6 +354,7 @@ version = "2.16.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.8"
+groups = ["base", "executable-dependencies"]
 files = [
     {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"},
     {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"},
@@ -354,6 +369,7 @@ version = "0.23.1"
 description = "The bidirectional mapping library for Python."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "bidict-0.23.1-py3-none-any.whl", hash = "sha256:5dae8d4d79b552a71cbabc7deb25dfe8ce710b17ff41711e13010ead2abfc3e5"},
     {file = "bidict-0.23.1.tar.gz", hash = "sha256:03069d763bc387bbd20e7d49914e75fc4132a41937fa3405417e1a5a2d006d71"},
@@ -361,17 +377,18 @@ files = [
 
 [[package]]
 name = "bluequbit"
-version = "0.8.1b1"
+version = "0.9.3b1"
 description = "Python SDK to BlueQubit app"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
-    {file = "bluequbit-0.8.1b1-py3-none-any.whl", hash = "sha256:0c6bd231f46db2a336fe1de0498f9faeafe2a3d13e8efda5e9568615c127e2a7"},
-    {file = "bluequbit-0.8.1b1.tar.gz", hash = "sha256:832f9c5b3f50c594659e470af5fda3fd33b0ebd5a3c5af40922db94eba314b86"},
+    {file = "bluequbit-0.9.3b1-py3-none-any.whl", hash = "sha256:2b9130ccced25b81b81fbef40247294c3804b14026aafbf0bac1ee795a545977"},
+    {file = "bluequbit-0.9.3b1.tar.gz", hash = "sha256:d695862ae350040ceadb2dc6dd78c0b10626b1b00b6d061fb1a0ed2ec568f788"},
 ]
 
 [package.dependencies]
-numpy = ">=1.21,<2.0"
+numpy = ">=1.21,<2.1"
 packaging = "*"
 python-dateutil = ">=2.8,<3.0"
 requests = ">=2.28,<3.0"
@@ -382,6 +399,7 @@ version = "5.5.0"
 description = "Extensible memoizing collections and decorators"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "cachetools-5.5.0-py3-none-any.whl", hash = "sha256:02134e8439cdc2ffb62023ce1debca2944c3f289d66bb17ead3ab3dede74b292"},
     {file = "cachetools-5.5.0.tar.gz", hash = "sha256:2cc24fb4cbe39633fb7badd9db9ca6295d766d9c2995f245725a46715d050f2a"},
@@ -389,13 +407,14 @@ files = [
 
 [[package]]
 name = "certifi"
-version = "2024.8.30"
+version = "2024.12.14"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
+groups = ["base", "executable-dependencies", "metadata-validation"]
 files = [
-    {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
-    {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
+    {file = "certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56"},
+    {file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"},
 ]
 
 [[package]]
@@ -404,6 +423,8 @@ version = "1.17.1"
 description = "Foreign Function Interface for Python calling C code."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
+markers = "platform_python_implementation != \"PyPy\" or implementation_name == \"pypy\""
 files = [
     {file = "cffi-1.17.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df8b1c11f177bc2313ec4b2d46baec87a5f3e71fc8b45dab2ee7cae86d9aba14"},
     {file = "cffi-1.17.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8f2cdc858323644ab277e9bb925ad72ae0e67f69e804f4898c070998d50b1a67"},
@@ -479,101 +500,104 @@ pycparser = "*"
 
 [[package]]
 name = "charset-normalizer"
-version = "3.3.2"
+version = "3.4.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
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 ]
 
 [[package]]
@@ -582,6 +606,7 @@ version = "0.23.3"
 description = "A jsonschema CLI and pre-commit hook"
 optional = false
 python-versions = ">=3.7"
+groups = ["metadata-validation"]
 files = [
     {file = "check-jsonschema-0.23.3.tar.gz", hash = "sha256:6b174599b5f1a208e7b22b3d1698708a6d948a2a90ab3413a025e5c0217ee056"},
     {file = "check_jsonschema-0.23.3-py3-none-any.whl", hash = "sha256:416d2a4be5e8a4a102074131e620b4262b2f1b494635a02792c4a008483794ca"},
@@ -599,22 +624,23 @@ docs = ["furo (==2023.5.20)", "sphinx (<8)", "sphinx-issues (<4)"]
 
 [[package]]
 name = "chex"
-version = "0.1.86"
+version = "0.1.88"
 description = "Chex: Testing made fun, in JAX!"
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
-    {file = "chex-0.1.86-py3-none-any.whl", hash = "sha256:251c20821092323a3d9c28e1cf80e4a58180978bec368f531949bd9847eee568"},
-    {file = "chex-0.1.86.tar.gz", hash = "sha256:e8b0f96330eba4144659e1617c0f7a57b161e8cbb021e55c6d5056c7378091d1"},
+    {file = "chex-0.1.88-py3-none-any.whl", hash = "sha256:234b61a5baa8132802e4b9c5657167d6c8a911d90a59a0bec47d537567e41b75"},
+    {file = "chex-0.1.88.tar.gz", hash = "sha256:565de897b1373232cdfca5e699f50fa49403d2c7d23f6c5a75a97ef713d2fe36"},
 ]
 
 [package.dependencies]
 absl-py = ">=0.9.0"
-jax = ">=0.4.16"
-jaxlib = ">=0.1.37"
+jax = ">=0.4.27"
+jaxlib = ">=0.4.27"
 numpy = ">=1.24.1"
 toolz = ">=0.9.0"
-typing-extensions = ">=4.2.0"
+typing_extensions = ">=4.2.0"
 
 [[package]]
 name = "cirq"
@@ -622,6 +648,7 @@ version = "1.0.0"
 description = "A framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits."
 optional = false
 python-versions = ">=3.7.0"
+groups = ["executable-dependencies"]
 files = [
     {file = "cirq-1.0.0-py3-none-any.whl", hash = "sha256:8d61feba1c8d2a37d82a53e2500fb2430ad8e0ca7c3e2562fbd554bea0d9d7f7"},
 ]
@@ -644,6 +671,7 @@ version = "1.0.0"
 description = "A Cirq package to simulate and connect to Alpine Quantum Technologies quantum computers"
 optional = false
 python-versions = ">=3.7.0"
+groups = ["executable-dependencies"]
 files = [
     {file = "cirq_aqt-1.0.0-py3-none-any.whl", hash = "sha256:db7af0b40ceded8a5951f74f36e2235a873df519078e069a6e4d00fda4acd82d"},
 ]
@@ -658,6 +686,7 @@ version = "1.0.0"
 description = "A framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits."
 optional = false
 python-versions = ">=3.7.0"
+groups = ["executable-dependencies"]
 files = [
     {file = "cirq_core-1.0.0-py3-none-any.whl", hash = "sha256:a1df552e99ab339121c40c7bf4d18ba542bba151dd8b5ac8c4c33b7b9d46ba10"},
 ]
@@ -683,6 +712,7 @@ version = "1.0.0"
 description = "The Cirq module that provides tools and access to the Google Quantum Computing Service"
 optional = false
 python-versions = ">=3.7.0"
+groups = ["executable-dependencies"]
 files = [
     {file = "cirq_google-1.0.0-py3-none-any.whl", hash = "sha256:8fd1df0fd97d1ee076c01a2b2ab86aac966692890fb2793a45ef2393a6ccc4ab"},
 ]
@@ -699,6 +729,7 @@ version = "1.0.0"
 description = "A Cirq package to simulate and connect to IonQ quantum computers"
 optional = false
 python-versions = ">=3.7.0"
+groups = ["executable-dependencies"]
 files = [
     {file = "cirq_ionq-1.0.0-py3-none-any.whl", hash = "sha256:31ff254a5b514736f79a92a186d473b3e40550c94ad36aeebddea1cb840fb065"},
 ]
@@ -713,6 +744,7 @@ version = "1.0.0"
 description = "A Cirq package to simulate and connect to Pasqal quantum computers"
 optional = false
 python-versions = ">=3.7.0"
+groups = ["executable-dependencies"]
 files = [
     {file = "cirq_pasqal-1.0.0-py3-none-any.whl", hash = "sha256:1243aae6514aadb6a5e2f729da3d99904d20631e7d2a7fbaef69c7377a9158b5"},
 ]
@@ -727,6 +759,7 @@ version = "1.0.0"
 description = "A Cirq package to simulate and connect to Rigetti quantum computers and Quil QVM"
 optional = false
 python-versions = ">=3.7.0"
+groups = ["executable-dependencies"]
 files = [
     {file = "cirq_rigetti-1.0.0-py3-none-any.whl", hash = "sha256:a0ff2ac5dea2b175ced45ade23bd2bfc2f1d8d280b6a003b1dcdddfdd579c0bd"},
 ]
@@ -741,6 +774,7 @@ version = "1.0.0"
 description = "Web-based 3D visualization tools for Cirq."
 optional = false
 python-versions = ">=3.7.0"
+groups = ["executable-dependencies"]
 files = [
     {file = "cirq_web-1.0.0-py3-none-any.whl", hash = "sha256:e3ebc6043b5a6a2a4ba57a71ea9ea5bc487732527b4512961e4c4ce90b2f5744"},
 ]
@@ -748,15 +782,38 @@ files = [
 [package.dependencies]
 cirq-core = "1.0.0"
 
+[[package]]
+name = "clarabel"
+version = "0.9.0"
+description = "Clarabel Conic Interior Point Solver for Rust / Python"
+optional = false
+python-versions = ">=3.7"
+groups = ["executable-dependencies"]
+files = [
+    {file = "clarabel-0.9.0-cp37-abi3-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:702cc4666c0ccf893c936f9f1f55cbb3233ae2d5fa05f67b370ac3e7ec50f222"},
+    {file = "clarabel-0.9.0-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:8ea616757b460153ead375b3dd3ce763d46fc3717248077bbfa7b2c844b1775f"},
+    {file = "clarabel-0.9.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2b5ae16d7dd87aabf72260cf9590ba0d037c52d48555bcf3a86b1f0d9cf88dd4"},
+    {file = "clarabel-0.9.0-cp37-abi3-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:85cb560a5c4cdfb079e3437e21f0b62b69ba766ae082aeb96ced0b5763214077"},
+    {file = "clarabel-0.9.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0eaeb3fbb5a90b598700d5435c7f102592a1a79ee25df5a097e0af575838786b"},
+    {file = "clarabel-0.9.0-cp37-abi3-win32.whl", hash = "sha256:759c2fa0ccc61ae1a02691c43753638a0ae793bf1de81c6f6763c346789a7e25"},
+    {file = "clarabel-0.9.0-cp37-abi3-win_amd64.whl", hash = "sha256:d24e4ed1b686eb2fe2a1b6e77935af6ad62a2c044131e70801ec1d3ef3d33280"},
+    {file = "clarabel-0.9.0.tar.gz", hash = "sha256:0d6d3fe8800be5b4b5d40a8e14bd492667b3e46cc5dbe37677ce5ed25f0719d4"},
+]
+
+[package.dependencies]
+numpy = "*"
+scipy = "*"
+
 [[package]]
 name = "click"
-version = "8.1.7"
+version = "8.1.8"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies", "metadata-validation"]
 files = [
-    {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
-    {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
+    {file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"},
+    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
 ]
 
 [package.dependencies]
@@ -764,13 +821,14 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cloudpickle"
-version = "3.0.0"
+version = "3.1.1"
 description = "Pickler class to extend the standard pickle.Pickler functionality"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
-    {file = "cloudpickle-3.0.0-py3-none-any.whl", hash = "sha256:246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7"},
-    {file = "cloudpickle-3.0.0.tar.gz", hash = "sha256:996d9a482c6fb4f33c1a35335cf8afd065d2a56e973270364840712d9131a882"},
+    {file = "cloudpickle-3.1.1-py3-none-any.whl", hash = "sha256:c8c5a44295039331ee9dad40ba100a9c7297b6f988e50e87ccdf3765a668350e"},
+    {file = "cloudpickle-3.1.1.tar.gz", hash = "sha256:b216fa8ae4019d5482a8ac3c95d8f6346115d8835911fd4aefd1a445e4242c64"},
 ]
 
 [[package]]
@@ -779,83 +837,75 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["base", "executable-dependencies", "metadata-validation"]
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
+markers = {base = "sys_platform == \"win32\"", executable-dependencies = "platform_system == \"Windows\" or sys_platform == \"win32\"", metadata-validation = "platform_system == \"Windows\""}
 
 [[package]]
 name = "contourpy"
-version = "1.3.0"
+version = "1.3.1"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
-python-versions = ">=3.9"
-files = [
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@@ -870,13 +920,14 @@ test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist"
 
 [[package]]
 name = "cotengra"
-version = "0.6.2"
+version = "0.7.0"
 description = "Hyper optimized contraction trees for large tensor networks and einsums."
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -884,8 +935,8 @@ autoray = "*"
 
 [package.extras]
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+recommended = ["cmaes", "cotengrust (>=0.1.3)", "cytoolz", "kahypar", "networkx", "numpy", "opt-einsum", "optuna", "ray", "tqdm"]
+test = ["altair", "baytune", "chocolate", "cmaes", "dask", "distributed", "kahypar", "matplotlib", "networkx", "nevergrad", "numpy", "opt-einsum", "pytest", "scikit-optimize", "seaborn"]
 
 [[package]]
 name = "covalent"
@@ -893,6 +944,7 @@ version = "0.227.0rc0"
 description = "Covalent Workflow Tool"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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 ]
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 [[package]]
 name = "cryptography"
-version = "43.0.1"
+version = "44.0.0"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
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 ]
 
 [package.dependencies]
@@ -1114,21 +1231,22 @@ cython = ["cython"]
 
 [[package]]
 name = "dask"
-version = "2024.9.0"
+version = "2025.1.0"
 description = "Parallel PyData with Task Scheduling"
 optional = false
 python-versions = ">=3.10"
+groups = ["executable-dependencies"]
 files = [
-    {file = "dask-2024.9.0-py3-none-any.whl", hash = "sha256:ceede9cfd418178a01ec3d11a0cde3f46678bd4a292ba84b57bbb401ce3f1cb8"},
-    {file = "dask-2024.9.0.tar.gz", hash = "sha256:bfbe5b6c3b7937426539be27029800178ce63cea4da8d7e7de836a98384aa1d6"},
+    {file = "dask-2025.1.0-py3-none-any.whl", hash = "sha256:db86220c8d19bdf464cbe11a87a2c8f5d537acf586bb02eed6d61a302af5c2fd"},
+    {file = "dask-2025.1.0.tar.gz", hash = "sha256:bb807586ff20f0f59f3d36fe34eb4a95f75a1aae2a775b521de6dd53727d2063"},
 ]
 
 [package.dependencies]
 click = ">=8.1"
 cloudpickle = ">=3.0.0"
-distributed = {version = "2024.9.0", optional = true, markers = "extra == \"distributed\""}
+distributed = {version = "2025.1.0", optional = true, markers = "extra == \"distributed\""}
 fsspec = ">=2021.09.0"
-importlib-metadata = {version = ">=4.13.0", markers = "python_version < \"3.12\""}
+importlib_metadata = {version = ">=4.13.0", markers = "python_version < \"3.12\""}
 packaging = ">=20.0"
 partd = ">=1.4.0"
 pyyaml = ">=5.3.1"
@@ -1137,10 +1255,10 @@ toolz = ">=0.10.0"
 [package.extras]
 array = ["numpy (>=1.24)"]
 complete = ["dask[array,dataframe,diagnostics,distributed]", "lz4 (>=4.3.2)", "pyarrow (>=14.0.1)"]
-dataframe = ["dask-expr (>=1.1,<1.2)", "dask[array]", "pandas (>=2.0)"]
+dataframe = ["dask[array]", "pandas (>=2.0)", "pyarrow (>=14.0.1)"]
 diagnostics = ["bokeh (>=3.1.0)", "jinja2 (>=2.10.3)"]
-distributed = ["distributed (==2024.9.0)"]
-test = ["pandas[test]", "pre-commit", "pytest", "pytest-cov", "pytest-rerunfailures", "pytest-timeout", "pytest-xdist"]
+distributed = ["distributed (==2025.1.0)"]
+test = ["pandas[test]", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytest-rerunfailures", "pytest-timeout", "pytest-xdist"]
 
 [[package]]
 name = "decorator"
@@ -1148,6 +1266,7 @@ version = "5.1.1"
 description = "Decorators for Humans"
 optional = false
 python-versions = ">=3.5"
+groups = ["executable-dependencies"]
 files = [
     {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
     {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
@@ -1155,20 +1274,21 @@ files = [
 
 [[package]]
 name = "deprecated"
-version = "1.2.14"
+version = "1.2.15"
 description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+groups = ["executable-dependencies"]
 files = [
-    {file = "Deprecated-1.2.14-py2.py3-none-any.whl", hash = "sha256:6fac8b097794a90302bdbb17b9b815e732d3c4720583ff1b198499d78470466c"},
-    {file = "Deprecated-1.2.14.tar.gz", hash = "sha256:e5323eb936458dccc2582dc6f9c322c852a775a27065ff2b0c4970b9d53d01b3"},
+    {file = "Deprecated-1.2.15-py2.py3-none-any.whl", hash = "sha256:353bc4a8ac4bfc96800ddab349d89c25dec1079f65fd53acdcc1e0b975b21320"},
+    {file = "deprecated-1.2.15.tar.gz", hash = "sha256:683e561a90de76239796e6b6feac66b99030d2dd3fcf61ef996330f14bbb9b0d"},
 ]
 
 [package.dependencies]
 wrapt = ">=1.10,<2"
 
 [package.extras]
-dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "jinja2 (>=3.0.3,<3.1.0)", "setuptools", "sphinx (<2)", "tox"]
 
 [[package]]
 name = "deprecation"
@@ -1176,6 +1296,7 @@ version = "2.1.0"
 description = "A library to handle automated deprecations"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
     {file = "deprecation-2.1.0-py2.py3-none-any.whl", hash = "sha256:a10811591210e1fb0e768a8c25517cabeabcba6f0bf96564f8ff45189f90b14a"},
     {file = "deprecation-2.1.0.tar.gz", hash = "sha256:72b3bde64e5d778694b0cf68178aed03d15e15477116add3fb773e581f9518ff"},
@@ -1190,6 +1311,7 @@ version = "2.15.2"
 description = "A library for Python operator overloading"
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
     {file = "diastatic-malt-2.15.2.tar.gz", hash = "sha256:7eb90d8c30b7ff16b4e84c3a65de2ff7f5b7b9d0f5cdea23918e747ff7fb5320"},
     {file = "diastatic_malt-2.15.2-py3-none-any.whl", hash = "sha256:85429257b356030f101c31b2c7d506c4829f21bd865aed796766f900d7908407"},
@@ -1202,13 +1324,14 @@ termcolor = "*"
 
 [[package]]
 name = "dill"
-version = "0.3.8"
+version = "0.3.9"
 description = "serialize all of Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
-    {file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"},
-    {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
+    {file = "dill-0.3.9-py3-none-any.whl", hash = "sha256:468dff3b89520b474c0397703366b7b95eebe6303f108adf9b19da1f702be87a"},
+    {file = "dill-0.3.9.tar.gz", hash = "sha256:81aa267dddf68cbfe8029c42ca9ec6a4ab3b22371d1c450abc54422577b4512c"},
 ]
 
 [package.extras]
@@ -1221,6 +1344,7 @@ version = "0.12.15"
 description = "A shared API for binary quadratic model samplers."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "dimod-0.12.15-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5586b7670dc6c4e9efa40a9d1ba6d0f66f0a78d240613a910cb6a461ea7ba47a"},
     {file = "dimod-0.12.15-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:da7f46b93a294e8b315ef192e7fb8a39583016745d83d5c5bfeba3e54810283f"},
@@ -1259,6 +1383,7 @@ version = "5.6.3"
 description = "Disk Cache -- Disk and file backed persistent cache."
 optional = false
 python-versions = ">=3"
+groups = ["executable-dependencies"]
 files = [
     {file = "diskcache-5.6.3-py3-none-any.whl", hash = "sha256:5e31b2d5fbad117cc363ebaf6b689474db18a1f6438bc82358b024abd4c2ca19"},
     {file = "diskcache-5.6.3.tar.gz", hash = "sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc"},
@@ -1266,19 +1391,20 @@ files = [
 
 [[package]]
 name = "distributed"
-version = "2024.9.0"
+version = "2025.1.0"
 description = "Distributed scheduler for Dask"
 optional = false
 python-versions = ">=3.10"
+groups = ["executable-dependencies"]
 files = [
-    {file = "distributed-2024.9.0-py3-none-any.whl", hash = "sha256:d3a6407efffd3ab70a7f3a068be0f15c7ba36adc4fc666ed3d3505ced2c79cbe"},
-    {file = "distributed-2024.9.0.tar.gz", hash = "sha256:cb5f76ff230fc2249b15b0a66da46982e23249e5d8389c16dfe427e598b71c57"},
+    {file = "distributed-2025.1.0-py3-none-any.whl", hash = "sha256:0b9c0ebcab8fe25322d71d04deb6d8ed8c7bbfd1521d1906d5d71bf82eee5ae3"},
+    {file = "distributed-2025.1.0.tar.gz", hash = "sha256:8924c49adae0fc8532b464e94bdfea979c08c67835bafb5f315f33cc0ab14dd3"},
 ]
 
 [package.dependencies]
 click = ">=8.0"
 cloudpickle = ">=3.0.0"
-dask = "2024.9.0"
+dask = "2025.1.0"
 jinja2 = ">=2.10.3"
 locket = ">=1.0.0"
 msgpack = ">=1.0.2"
@@ -1298,6 +1424,7 @@ version = "0.1.8"
 description = "Tree is a library for working with nested data structures."
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
     {file = "dm-tree-0.1.8.tar.gz", hash = "sha256:0fcaabbb14e7980377439e7140bd05552739ca5e515ecb3119f234acee4b9430"},
     {file = "dm_tree-0.1.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:35cc164a79336bfcfafb47e5f297898359123bbd3330c1967f0c4994f9cf9f60"},
@@ -1353,6 +1480,7 @@ version = "2.25.236"
 description = "The IBM Decision Optimization CPLEX Modeling for Python"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
     {file = "docplex-2.25.236.tar.gz", hash = "sha256:256914b4c0113a4e1c78f32ea20c7d76dc8efe286fe890270e75cfad50fe510f"},
 ]
@@ -1366,6 +1494,7 @@ version = "0.17.1"
 description = "Docutils -- Python Documentation Utilities"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["base", "executable-dependencies"]
 files = [
     {file = "docutils-0.17.1-py2.py3-none-any.whl", hash = "sha256:cf316c8370a737a022b72b56874f6602acf974a37a9fba42ec2876387549fc61"},
     {file = "docutils-0.17.1.tar.gz", hash = "sha256:686577d2e4c32380bb50cbb22f575ed742d58168cee37e99117a854bcd88f125"},
@@ -1377,6 +1506,7 @@ version = "0.2.9"
 description = "A simple future-based async library for python."
 optional = false
 python-versions = ">=3.9.0"
+groups = ["executable-dependencies"]
 files = [
     {file = "duet-0.2.9-py3-none-any.whl", hash = "sha256:a16088b68b0faee8aee12cdf4d0a8af060ed958badb44f3e32f123f13f64119a"},
     {file = "duet-0.2.9.tar.gz", hash = "sha256:d6fa39582e6a3dce1096c47e5fbcbda648a633eed94a38943e68662afa2587f3"},
@@ -1391,6 +1521,7 @@ version = "0.12.0"
 description = "A minimal client for interacting with D-Wave cloud resources."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwave-cloud-client-0.12.0.tar.gz", hash = "sha256:9eb8cc3075f1df956e70a8e6ac9aca120892d2100b007db515d9f2b99b01aa45"},
     {file = "dwave_cloud_client-0.12.0-py3-none-any.whl", hash = "sha256:21b45ff1a093c72ed0b4d87f6934e5dae9aaf3574efa285a764164b1210ca4d8"},
@@ -1427,6 +1558,7 @@ version = "0.3.0"
 description = "Ocean-compatible collection of greedy/brute-force solvers/samplers"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwave-greedy-0.3.0.tar.gz", hash = "sha256:806c02d6c3f260ef5bf063d4932f43dfb52a0ea00f9db0b0d34d9b15691a011a"},
     {file = "dwave_greedy-0.3.0-py3-none-any.whl", hash = "sha256:34c2d24bf6d4f4d3cee532dbc85c79b7faa4845a7bd51435fc4114d077095f6f"},
@@ -1441,6 +1573,7 @@ version = "0.6.11"
 description = "Hybrid Asynchronous Decomposition Solver Framework"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwave-hybrid-0.6.11.tar.gz", hash = "sha256:201230a937f92bd820311bdd6dd6ce8b68ff4e35392fe88e31aa8e41849c4eb9"},
     {file = "dwave_hybrid-0.6.11-py3-none-any.whl", hash = "sha256:2c84c0e999bb6332f2868e28f324aeb6ac0f6faa6741334499b4e45542774a2f"},
@@ -1470,6 +1603,7 @@ version = "0.5.0.post0"
 description = "D-Wave Problem Inspector tool"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwave-inspector-0.5.0.post0.tar.gz", hash = "sha256:c36bbc5cc7cb01f0f8168d7e4770f4e28fd468b1c90fc3d1e7c6e7d1ea7c4838"},
     {file = "dwave_inspector-0.5.0.post0-py3-none-any.whl", hash = "sha256:bcb12fd254add32c691875b89614f5345204148d0d2f9ec6a9a20ed5cc92163d"},
@@ -1492,6 +1626,7 @@ version = "0.6.0"
 description = "General Ising graph simulated annealing solver"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwave-neal-0.6.0.tar.gz", hash = "sha256:8ce51fee3339195df1ab69920fdb5afc496b5fd945e487fad3547c983d90c564"},
     {file = "dwave_neal-0.6.0-py3-none-any.whl", hash = "sha256:8b7d89f0c52de6ac80e0f580ec272f6409b1cf9edb12250d22429425a13bd935"},
@@ -1506,6 +1641,7 @@ version = "0.8.15"
 description = "A NetworkX extension providing graphs and algorithms relevent to working with the D-Wave System"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwave_networkx-0.8.15-py3-none-any.whl", hash = "sha256:e74ac925b8128e78f4eedc8e2cc78c13ca11474b30cc77a7129cc8a16fdc4312"},
     {file = "dwave_networkx-0.8.15.tar.gz", hash = "sha256:ecafcd7d12e257d771b8a0522c6dc8794be310c112d49768c1caf804ab54b423"},
@@ -1522,6 +1658,7 @@ version = "7.0.0"
 description = "Software development kit for open source D-Wave tools"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwave_ocean_sdk-7.0.0-py3-none-any.whl", hash = "sha256:d5e9ecb48671eab66dcb79e0be6ea76eecf4c79d0cfb6add6f4e004f8e6bd57a"},
     {file = "dwave_ocean_sdk-7.0.0.tar.gz", hash = "sha256:891e0fb30dd693ce8435eab61ab2490a5101d9c929e26bd4be2a94257c4b71a3"},
@@ -1553,6 +1690,7 @@ version = "0.1.0"
 description = "Enables the formulation of nonlinear models for industrial optimization problems."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwave-optimization-0.1.0.tar.gz", hash = "sha256:585b69026d26e9f99b9c8552a68dae794960cd94582ccdf973d2eb30a6303b9e"},
     {file = "dwave_optimization-0.1.0-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:d356133dc4f1b8ede0fb2479e8b263084c958d83b8a5e420f02cf522d5aa32b1"},
@@ -1591,6 +1729,7 @@ version = "0.6.5"
 description = "An API for preprocessing tools"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwave-preprocessing-0.6.5.tar.gz", hash = "sha256:dc18e544a8b743e1bbc9e776129c0709247d071e7ec44229df48d92bb993a1be"},
     {file = "dwave_preprocessing-0.6.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:86fb8c39c8a8f025683e838b1f23608cd3ab4e3ca7f929f42b3b99036272ebd4"},
@@ -1630,6 +1769,7 @@ version = "1.2.0"
 description = "Ocean-compatible collection of solvers/samplers"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwave-samplers-1.2.0.tar.gz", hash = "sha256:b85e3f0486ddc03bfc936791707cf40e07df1a6eb68843d7329e8539fb41b077"},
     {file = "dwave_samplers-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:601393f25dd32d11b7f2f12a833b86f16a1500771c6a191c0b2db638d6ecca79"},
@@ -1670,6 +1810,7 @@ version = "1.25.0"
 description = "All things D-Wave System."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwave-system-1.25.0.tar.gz", hash = "sha256:0618f5b63ebe7f04646fc6a02982bee2a0504c7b75ac17693cf85b0dc46a9513"},
     {file = "dwave_system-1.25.0-py3-none-any.whl", hash = "sha256:fe2707ea8911c5a86850ed60b91bcc1fa11e0b0f4300bd8c45bd04ce05b59525"},
@@ -1696,6 +1837,7 @@ version = "0.5.0"
 description = "Optimized Tabu solver for QUBOs"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwave-tabu-0.5.0.tar.gz", hash = "sha256:dcc73883ec491dea2656c8239dc66e9c9871e7f75852be57ea4cec6bccd48d54"},
     {file = "dwave_tabu-0.5.0-py3-none-any.whl", hash = "sha256:ce63c4e8cee278cc3b7e82d9c6c7ff6fb84c50cb45eba12dc0e4c299c46eb784"},
@@ -1710,6 +1852,7 @@ version = "0.3.0"
 description = "Solves constraints satisfaction problems with binary quadratic model samplers"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "dwavebinarycsp-0.3.0-py3-none-any.whl", hash = "sha256:04e4ce545d5f6467885436f52b40d247a6ba8e690dbb303d669ea953b789f869"},
     {file = "dwavebinarycsp-0.3.0.tar.gz", hash = "sha256:0e34261e85814bf92dcdacba854c31d2ed4e9f17412bf933810ad6eef3f8e4ef"},
@@ -1722,25 +1865,29 @@ penaltymodel = ">=1.0.0"
 
 [[package]]
 name = "etils"
-version = "1.9.4"
+version = "1.11.0"
 description = "Collection of common python utils"
 optional = false
 python-versions = ">=3.10"
+groups = ["executable-dependencies"]
 files = [
-    {file = "etils-1.9.4-py3-none-any.whl", hash = "sha256:4387e7a4911a3b5cc4b92b99a9211386d176b43bae1dac8e2fe345fc2cb95e4b"},
-    {file = "etils-1.9.4.tar.gz", hash = "sha256:fad950414f0a1ca58c70c70915b0014f9953dd9bcf8aa951a0f75ff9becbeb24"},
+    {file = "etils-1.11.0-py3-none-any.whl", hash = "sha256:a394cf3476bcec51c221426a70c39cd1006e889456ba41e4d7f12fd6814be7a5"},
+    {file = "etils-1.11.0.tar.gz", hash = "sha256:aff3278a3be7fddf302dfd80335e9f924244666c71239cd91e836f3d055f1c4a"},
 ]
 
 [package.dependencies]
 fsspec = {version = "*", optional = true, markers = "extra == \"epath\""}
 importlib_resources = {version = "*", optional = true, markers = "extra == \"epath\""}
-typing_extensions = {version = "*", optional = true, markers = "extra == \"epath\" or extra == \"epy\""}
+typing_extensions = [
+    {version = "*", optional = true, markers = "extra == \"epy\""},
+    {version = "*", optional = true, markers = "extra == \"epath\" or extra == \"epy\""},
+]
 zipp = {version = "*", optional = true, markers = "extra == \"epath\""}
 
 [package.extras]
 all = ["etils[array-types]", "etils[eapp]", "etils[ecolab]", "etils[edc]", "etils[enp]", "etils[epath-gcs]", "etils[epath-s3]", "etils[epath]", "etils[epy]", "etils[etqdm]", "etils[etree-dm]", "etils[etree-jax]", "etils[etree-tf]", "etils[etree]"]
 array-types = ["etils[enp]"]
-dev = ["chex", "optree", "pydantic", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-subtests", "pytest-xdist", "tensorflow_datasets", "torch"]
+dev = ["chex", "fiddle", "optree", "pydantic", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-subtests", "pytest-xdist", "tensorflow_datasets", "torch"]
 docs = ["etils[all,dev]", "sphinx-apitree[ext]"]
 eapp = ["absl-py", "etils[epy]", "simple_parsing"]
 ecolab = ["etils[enp]", "etils[epy]", "etils[etree]", "jupyter", "mediapy", "numpy", "packaging", "protobuf"]
@@ -1763,6 +1910,7 @@ version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
     {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
@@ -1777,6 +1925,7 @@ version = "2.1.0"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "executing-2.1.0-py2.py3-none-any.whl", hash = "sha256:8d63781349375b5ebccc3142f4b30350c0cd9c79f921cde38be2be4637e98eaf"},
     {file = "executing-2.1.0.tar.gz", hash = "sha256:8ea27ddd260da8150fa5a708269c4a10e76161e2496ec3e587da9e3c0fe4b9ab"},
@@ -1787,18 +1936,19 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastapi"
-version = "0.115.0"
+version = "0.115.6"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
-    {file = "fastapi-0.115.0-py3-none-any.whl", hash = "sha256:17ea427674467486e997206a5ab25760f6b09e069f099b96f5b55a32fb6f1631"},
-    {file = "fastapi-0.115.0.tar.gz", hash = "sha256:f93b4ca3529a8ebc6fc3fcf710e5efa8de3df9b41570958abf1d97d843138004"},
+    {file = "fastapi-0.115.6-py3-none-any.whl", hash = "sha256:e9240b29e36fa8f4bb7290316988e90c381e5092e0cbe84e7818cc3713bcf305"},
+    {file = "fastapi-0.115.6.tar.gz", hash = "sha256:9ec46f7addc14ea472958a96aae5b5de65f39721a46aaf5705c480d9a8b76654"},
 ]
 
 [package.dependencies]
 pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
-starlette = ">=0.37.2,<0.39.0"
+starlette = ">=0.40.0,<0.42.0"
 typing-extensions = ">=4.8.0"
 
 [package.extras]
@@ -1811,6 +1961,7 @@ version = "0.19"
 description = "A python package that provides useful locks"
 optional = false
 python-versions = ">=3.6"
+groups = ["executable-dependencies"]
 files = [
     {file = "fasteners-0.19-py3-none-any.whl", hash = "sha256:758819cb5d94cdedf4e836988b74de396ceacb8e2794d21f82d131fd9ee77237"},
     {file = "fasteners-0.19.tar.gz", hash = "sha256:b4f37c3ac52d8a445af3a66bce57b33b5e90b97c696b7b984f530cf8f0ded09c"},
@@ -1822,6 +1973,7 @@ version = "3.16.1"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "filelock-3.16.1-py3-none-any.whl", hash = "sha256:2082e5703d51fbf98ea75855d9d5527e33d8ff23099bec374a134febee6946b0"},
     {file = "filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435"},
@@ -1838,6 +1990,8 @@ version = "0.10.1b1"
 description = "FlamingPy is a cross-platform Python library with a variety of backends for efficient simulations of error correction in fault-tolerant quantum computers."
 optional = false
 python-versions = ">=3.8,!=3.11.*"
+groups = ["executable-dependencies"]
+markers = "platform_machine == \"x86_64\""
 files = [
     {file = "flamingpy-0.10.1b1-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:485001005b135a445ca42faa530cb3cd84524278923c31ecc59c28baab4320bb"},
     {file = "flamingpy-0.10.1b1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:5052fcb9100126c916c7cfe5cf2e83bc5e3a57a87d8307cc9d8896cbbf021333"},
@@ -1865,6 +2019,7 @@ version = "2.2.5"
 description = "A simple framework for building complex web applications."
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "Flask-2.2.5-py3-none-any.whl", hash = "sha256:58107ed83443e86067e41eff4631b058178191a355886f8e479e347fa1285fdf"},
     {file = "Flask-2.2.5.tar.gz", hash = "sha256:edee9b0a7ff26621bd5a8c10ff484ae28737a2410d99b0bb9a6850c7fb977aa0"},
@@ -1882,13 +2037,14 @@ dotenv = ["python-dotenv"]
 
 [[package]]
 name = "flatbuffers"
-version = "24.3.25"
+version = "24.12.23"
 description = "The FlatBuffers serialization format for Python"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
-    {file = "flatbuffers-24.3.25-py2.py3-none-any.whl", hash = "sha256:8dbdec58f935f3765e4f7f3cf635ac3a77f83568138d6a2311f524ec96364812"},
-    {file = "flatbuffers-24.3.25.tar.gz", hash = "sha256:de2ec5b203f21441716617f38443e0a8ebf3d25bf0d9c0bb0ce68fa00ad546a4"},
+    {file = "flatbuffers-24.12.23-py2.py3-none-any.whl", hash = "sha256:c418e0d48890f4142b92fd3e343e73a48f194e1f80075ddcc5793779b3585444"},
+    {file = "flatbuffers-24.12.23.tar.gz", hash = "sha256:2910b0bc6ae9b6db78dd2b18d0b7a0709ba240fb5585f286a3a2b30785c22dac"},
 ]
 
 [[package]]
@@ -1897,6 +2053,7 @@ version = "0.9.0"
 description = "Flax: A neural network library for JAX designed for flexibility"
 optional = false
 python-versions = ">=3.10"
+groups = ["executable-dependencies"]
 files = [
     {file = "flax-0.9.0-py3-none-any.whl", hash = "sha256:12cd8f7162165ddd56877fb1cd9a4fcb47a31569e4c5343eeb59a36369fa2cfe"},
     {file = "flax-0.9.0.tar.gz", hash = "sha256:8b7f361eed0f5324e81f9dc8d02ea53da5f993d7c2e37e7aa5b37d3f6331dd53"},
@@ -1920,59 +2077,62 @@ testing = ["clu", "clu (<=0.0.9)", "einops", "gymnasium[accept-rom-license,atari
 
 [[package]]
 name = "fonttools"
-version = "4.54.1"
+version = "4.55.3"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
-files = [
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 ]
 
 [[package]]
@@ -2134,6 +2318,7 @@ version = "2024.6.1"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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@@ -2173,6 +2358,7 @@ version = "2.1.3"
 description = "URL manipulation made simple."
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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@@ -2188,6 +2374,7 @@ version = "0.6.0"
 description = "Python AST that abstracts the underlying Python version"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+groups = ["executable-dependencies"]
 files = [
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@@ -2199,6 +2386,7 @@ version = "1.34.1"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
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@@ -2219,13 +2407,14 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"]
 
 [[package]]
 name = "google-auth"
-version = "2.35.0"
+version = "2.37.0"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
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 ]
 
 [package.dependencies]
@@ -2236,6 +2425,7 @@ rsa = ">=3.1.4,<5"
 [package.extras]
 aiohttp = ["aiohttp (>=3.6.2,<4.0.0.dev0)", "requests (>=2.20.0,<3.0.0.dev0)"]
 enterprise-cert = ["cryptography", "pyopenssl"]
+pyjwt = ["cryptography (>=38.0.3)", "pyjwt (>=2.0)"]
 pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
 reauth = ["pyu2f (>=0.1.5)"]
 requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
@@ -2246,6 +2436,7 @@ version = "1.0.0"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.6"
+groups = ["executable-dependencies"]
 files = [
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@@ -2264,6 +2455,7 @@ version = "0.2.0"
 description = "pasta is an AST-based Python refactoring library"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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@@ -2275,13 +2467,14 @@ six = "*"
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.65.0"
+version = "1.66.0"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
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 ]
 
 [package.dependencies]
@@ -2296,6 +2489,8 @@ version = "3.1.1"
 description = "Lightweight in-process concurrent programming"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
+markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\" or platform_machine == \"x86_64\""
 files = [
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@@ -2378,61 +2573,71 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "grpcio"
-version = "1.66.1"
+version = "1.69.0"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.8"
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 name = "grpcio-status"
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 description = "Status proto mapping for gRPC"
 optional = false
 python-versions = ">=3.6"
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 python-versions = ">=3.8"
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 python-versions = "*"
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+version = "3.22.1"
 description = "Core library used by SDKs for IBM Cloud Services"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
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 [package.dependencies]
 PyJWT = ">=2.8.0,<3.0.0"
-python-dateutil = ">=2.8.2,<3.0.0"
+python_dateutil = ">=2.8.2,<3.0.0"
 requests = ">=2.31.0,<3.0.0"
 urllib3 = ">=2.1.0,<3.0.0"
 
@@ -2576,17 +2794,18 @@ publish = ["build", "twine"]
 
 [[package]]
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-version = "0.57.1"
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 ]
 
 [package.dependencies]
-ibm-cloud-sdk-core = ">=3.21.0,<4.0.0"
+ibm_cloud_sdk_core = ">=3.22.0,<4.0.0"
 
 [package.extras]
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@@ -2598,6 +2817,7 @@ version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.6"
+groups = ["base", "executable-dependencies", "metadata-validation"]
 files = [
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@@ -2612,6 +2832,7 @@ version = "1.4.1"
 description = "Getting image size from png/jpeg/jpeg2000/gif file"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["base", "executable-dependencies"]
 files = [
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@@ -2623,6 +2844,7 @@ version = "8.5.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "importlib_metadata-8.5.0-py3-none-any.whl", hash = "sha256:45e54197d28b7a7f1559e60b95e7c567032b602131fbd588f1497f47880aa68b"},
     {file = "importlib_metadata-8.5.0.tar.gz", hash = "sha256:71522656f0abace1d072b9e5481a48f07c138e00f079c38c8f883823f9c26bd7"},
@@ -2642,13 +2864,14 @@ type = ["pytest-mypy"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.4.5"
+version = "6.5.2"
 description = "Read resources from Python packages"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
+groups = ["base", "executable-dependencies"]
 files = [
-    {file = "importlib_resources-6.4.5-py3-none-any.whl", hash = "sha256:ac29d5f956f01d5e4bb63102a5a19957f1b9175e45649977264a1416783bb717"},
-    {file = "importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065"},
+    {file = "importlib_resources-6.5.2-py3-none-any.whl", hash = "sha256:789cfdc3ed28c78b67a06acb8126751ced69a3d5f79c095a98298cd8a760ccec"},
+    {file = "importlib_resources-6.5.2.tar.gz", hash = "sha256:185f87adef5bcc288449d98fb4fba07cea78bc036455dd44c5fc4a2fe78fed2c"},
 ]
 
 [package.extras]
@@ -2665,6 +2888,7 @@ version = "2.0.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
@@ -2672,13 +2896,14 @@ files = [
 
 [[package]]
 name = "ipython"
-version = "8.27.0"
+version = "8.31.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
+groups = ["executable-dependencies"]
 files = [
-    {file = "ipython-8.27.0-py3-none-any.whl", hash = "sha256:f68b3cb8bde357a5d7adc9598d57e22a45dfbea19eb6b98286fa3b288c9cd55c"},
-    {file = "ipython-8.27.0.tar.gz", hash = "sha256:0b99a2dc9f15fd68692e898e5568725c6d49c527d36a9fb5960ffbdeaa82ff7e"},
+    {file = "ipython-8.31.0-py3-none-any.whl", hash = "sha256:46ec58f8d3d076a61d128fe517a51eb730e3aaf0c184ea8c17d16e366660c6a6"},
+    {file = "ipython-8.31.0.tar.gz", hash = "sha256:b6a2274606bec6166405ff05e54932ed6e5cfecaca1fc05f2cacde7bb074d70b"},
 ]
 
 [package.dependencies]
@@ -2688,16 +2913,16 @@ exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
-prompt-toolkit = ">=3.0.41,<3.1.0"
+prompt_toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
-stack-data = "*"
+stack_data = "*"
 traitlets = ">=5.13.0"
-typing-extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
+typing_extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
 
 [package.extras]
 all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
 black = ["black"]
-doc = ["docrepr", "exceptiongroup", "intersphinx-registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli", "typing_extensions"]
 kernel = ["ipykernel"]
 matplotlib = ["matplotlib"]
 nbconvert = ["nbconvert"]
@@ -2708,12 +2933,42 @@ qtconsole = ["qtconsole"]
 test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
 test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
+[[package]]
+name = "iqpopt"
+version = "2024.7.0"
+description = "IQP circuit simulation"
+optional = false
+python-versions = "*"
+groups = ["executable-dependencies"]
+files = []
+develop = false
+
+[package.dependencies]
+cvxpy = "*"
+jax = "*"
+jaxopt = "*"
+matplotlib = "*"
+numpy = "*"
+numpyro = "*"
+optax = "*"
+pandas = "*"
+pennylane = "*"
+scipy = "*"
+tqdm = "*"
+
+[package.source]
+type = "git"
+url = "https://github.com/XanaduAI/iqpopt.git"
+reference = "HEAD"
+resolved_reference = "1adf46a571309ab53cc252f1febdd61f45bc1e66"
+
 [[package]]
 name = "isoduration"
 version = "20.11.0"
 description = "Operations with ISO 8601 durations"
 optional = false
 python-versions = ">=3.7"
+groups = ["metadata-validation"]
 files = [
     {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"},
     {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"},
@@ -2728,6 +2983,7 @@ version = "2.2.0"
 description = "Safely pass data to untrusted environments and back."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "itsdangerous-2.2.0-py3-none-any.whl", hash = "sha256:c6242fc49e35958c8b15141343aa660db5fc54d4f13a1db01a3f5891b98700ef"},
     {file = "itsdangerous-2.2.0.tar.gz", hash = "sha256:e0050c0b7da1eea53ffaf149c0cfbb5c6e2e2b69c4bef22c81fa6eb73e5f6173"},
@@ -2739,6 +2995,7 @@ version = "0.4.28"
 description = "Differentiate, compile, and transform Numpy code."
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
     {file = "jax-0.4.28-py3-none-any.whl", hash = "sha256:6a181e6b5a5b1140e19cdd2d5c4aa779e4cb4ec627757b918be322d8e81035ba"},
     {file = "jax-0.4.28.tar.gz", hash = "sha256:dcf0a44aff2e1713f0a2b369281cd5b79d8c18fc1018905c4125897cb06b37e9"},
@@ -2768,6 +3025,7 @@ version = "0.4.28"
 description = "XLA library for JAX"
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
     {file = "jaxlib-0.4.28-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:a421d237f8c25d2850166d334603c673ddb9b6c26f52bc496704b8782297bd66"},
     {file = "jaxlib-0.4.28-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f038e68bd10d1a3554722b0bbe36e6a448384437a75aa9d283f696f0ed9f8c09"},
@@ -2805,6 +3063,7 @@ version = "0.8.3"
 description = "Hardware accelerated, batchable and differentiable optimizers in JAX."
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
     {file = "jaxopt-0.8.3-py3-none-any.whl", hash = "sha256:4be2f82798393682529c9ca5046e5397ac6c8657b8acb6bf275e773b28df15b6"},
     {file = "jaxopt-0.8.3.tar.gz", hash = "sha256:4b06dfa6f915a4f3291699606245af6069371a48dc5c92d4c507840d62990646"},
@@ -2818,22 +3077,23 @@ scipy = ">=1.0.0"
 
 [[package]]
 name = "jedi"
-version = "0.19.1"
+version = "0.19.2"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
+groups = ["executable-dependencies"]
 files = [
-    {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
-    {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
+    {file = "jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9"},
+    {file = "jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0"},
 ]
 
 [package.dependencies]
-parso = ">=0.8.3,<0.9.0"
+parso = ">=0.8.4,<0.9.0"
 
 [package.extras]
 docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
-testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jinja2"
@@ -2841,6 +3101,7 @@ version = "3.0.3"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.6"
+groups = ["base", "executable-dependencies"]
 files = [
     {file = "Jinja2-3.0.3-py3-none-any.whl", hash = "sha256:077ce6014f7b40d03b47d1f1ca4b0fc8328a692bd284016f806ed0eaca390ad8"},
     {file = "Jinja2-3.0.3.tar.gz", hash = "sha256:611bb273cd68f3b993fabdc4064fc858c5b47a973cb5aa7999ec1ba405c87cd7"},
@@ -2858,6 +3119,8 @@ version = "1.4.2"
 description = "Lightweight pipelining with Python functions"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
+markers = "platform_machine == \"x86_64\""
 files = [
     {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
     {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
@@ -2869,6 +3132,7 @@ version = "3.0.0"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
 optional = false
 python-versions = ">=3.7"
+groups = ["metadata-validation"]
 files = [
     {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
     {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
@@ -2880,6 +3144,7 @@ version = "4.17.3"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.7"
+groups = ["metadata-validation"]
 files = [
     {file = "jsonschema-4.17.3-py3-none-any.whl", hash = "sha256:a870ad254da1a8ca84b6a2905cac29d265f805acc57af304784962a2aa6508f6"},
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@@ -2907,6 +3172,7 @@ version = "1.1.7"
 description = "Python Inferface for the Karlsruhe Hypergraph Partitioning Framework (KaHyPar)"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
     {file = "kahypar-1.1.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e72e1be11b0791076106502d17bdddd82973853356bbb5d472c874d55fd75327"},
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@@ -2934,6 +3200,7 @@ version = "2.14.0"
 description = "Deep learning for humans."
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
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@@ -2941,125 +3208,92 @@ files = [
 
 [[package]]
 name = "kiwisolver"
-version = "1.4.7"
+version = "1.4.8"
 description = "A fast implementation of the Cassowary constraint solver"
 optional = false
-python-versions = ">=3.8"
-files = [
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 optional = false
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 ]
 
 [[package]]
@@ -3132,6 +3369,7 @@ version = "1.0.0"
 description = "File-based locks for Python on Linux and Windows"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["executable-dependencies"]
 files = [
     {file = "locket-1.0.0-py2.py3-none-any.whl", hash = "sha256:b6c819a722f7b6bd955b80781788e4a66a55628b858d347536b7e81325a3a5e3"},
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@@ -3139,13 +3377,14 @@ files = [
 
 [[package]]
 name = "mako"
-version = "1.3.5"
+version = "1.3.8"
 description = "A super-fast templating language that borrows the best ideas from the existing templating languages."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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-    {file = "Mako-1.3.5.tar.gz", hash = "sha256:48dbc20568c1d276a2698b36d968fa76161bf127194907ea6fc594fa81f943bc"},
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+    {file = "mako-1.3.8.tar.gz", hash = "sha256:577b97e414580d3e088d47c2dbbe9594aa7a5146ed2875d4dfa9075af2dd3cc8"},
 ]
 
 [package.dependencies]
@@ -3162,6 +3401,7 @@ version = "3.7"
 description = "Python implementation of John Gruber's Markdown."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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@@ -3177,6 +3417,7 @@ version = "2.2.0"
 description = "Python port of markdown-it. Markdown parsing, done right!"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
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@@ -3201,6 +3442,7 @@ version = "2.1.1"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = false
 python-versions = ">=3.7"
+groups = ["base", "executable-dependencies"]
 files = [
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@@ -3250,6 +3492,7 @@ version = "3.7.2"
 description = "Python plotting package"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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@@ -3311,6 +3554,7 @@ version = "0.1.7"
 description = "Inline Matplotlib backend for Jupyter"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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@@ -3325,6 +3569,7 @@ version = "0.1.2"
 description = "Markdown URL utilities"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
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@@ -3336,6 +3581,7 @@ version = "0.2.14"
 description = "heuristic algorithm to find graph minor embeddings"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "minorminer-0.2.14-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7f709b8a1716f04fb63edcbce8724da0b1ba7da253db296a17383d6a107cb4dc"},
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@@ -3379,6 +3625,7 @@ version = "0.32.0"
 description = "Mitiq is an open source toolkit for implementing error mitigation techniques on most current intermediate-scale quantum computers."
 optional = false
 python-versions = ">=3.9,<3.12"
+groups = ["executable-dependencies"]
 files = [
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@@ -3399,6 +3646,7 @@ version = "0.2.0"
 description = ""
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
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@@ -3431,6 +3679,7 @@ version = "1.3.0"
 description = "Python library for arbitrary-precision floating-point arithmetic"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
     {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
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@@ -3448,6 +3697,7 @@ version = "1.1.0"
 description = "MessagePack serializer"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7ad442d527a7e358a469faf43fda45aaf4ac3249c8310a82f0ccff9164e5dccd"},
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@@ -3521,6 +3771,7 @@ version = "6.1.0"
 description = "multidict implementation"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "multidict-6.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3380252550e372e8511d49481bd836264c009adb826b23fefcc5dd3c69692f60"},
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@@ -3619,12 +3870,25 @@ files = [
 [package.dependencies]
 typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
 
+[[package]]
+name = "multipledispatch"
+version = "1.0.0"
+description = "Multiple dispatch"
+optional = false
+python-versions = "*"
+groups = ["executable-dependencies"]
+files = [
+    {file = "multipledispatch-1.0.0-py3-none-any.whl", hash = "sha256:0c53cd8b077546da4e48869f49b13164bebafd0c2a5afceb6bb6a316e7fb46e4"},
+    {file = "multipledispatch-1.0.0.tar.gz", hash = "sha256:5c839915465c68206c3e9c473357908216c28383b425361e5d144594bf85a7e0"},
+]
+
 [[package]]
 name = "natsort"
 version = "8.4.0"
 description = "Simple yet flexible natural sorting in Python."
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
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@@ -3640,6 +3904,7 @@ version = "1.6.0"
 description = "Patch asyncio to allow nested event loops"
 optional = false
 python-versions = ">=3.5"
+groups = ["executable-dependencies"]
 files = [
     {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"},
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@@ -3651,6 +3916,7 @@ version = "2.8.8"
 description = "Python package for creating and manipulating graphs and networks"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "networkx-2.8.8-py3-none-any.whl", hash = "sha256:e435dfa75b1d7195c7b8378c3859f0445cd88c6b0375c181ed66823a9ceb7524"},
     {file = "networkx-2.8.8.tar.gz", hash = "sha256:230d388117af870fce5647a3c52401fcf753e94720e6ea6b4197a5355648885e"},
@@ -3669,6 +3935,7 @@ version = "0.6.2"
 description = "Fast and Easy Infinite Neural Networks in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "neural-tangents-0.6.2.tar.gz", hash = "sha256:16c2ca70b8877685ffa30befab13a8e5966d45fbfabb422ef75cd62a0d243ba8"},
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@@ -3685,37 +3952,38 @@ testing = ["flax (>=0.5.2)", "more-itertools", "tensorflow-datasets"]
 
 [[package]]
 name = "numba"
-version = "0.60.0"
+version = "0.61.0"
 description = "compiling Python code using LLVM"
 optional = false
-python-versions = ">=3.9"
-files = [
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+llvmlite = "==0.44.*"
+numpy = ">=1.24,<2.2"
 
 [[package]]
 name = "numpy"
@@ -3723,6 +3991,7 @@ version = "1.24.4"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["base", "executable-dependencies"]
 files = [
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@@ -3754,12 +4023,41 @@ files = [
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 ]
 
+[[package]]
+name = "numpyro"
+version = "0.16.1"
+description = "Pyro PPL on NumPy"
+optional = false
+python-versions = ">=3.9"
+groups = ["executable-dependencies"]
+files = [
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+]
+
+[package.dependencies]
+jax = ">=0.4.25"
+jaxlib = ">=0.4.25"
+multipledispatch = "*"
+numpy = "*"
+tqdm = "*"
+
+[package.extras]
+cpu = ["jax[cpu] (>=0.4.25)"]
+cuda = ["jax[cuda] (>=0.4.25)"]
+dev = ["dm-haiku", "flax", "funsor (>=0.4.1)", "graphviz", "jaxns (==2.6.3)", "matplotlib", "optax (>=0.0.6)", "pylab-sdk", "pytest-cov", "pyyaml", "requests", "tensorflow_probability (>=0.18.0)"]
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+examples = ["arviz", "jupyter", "matplotlib", "pandas", "scikit-learn", "seaborn", "wordcloud"]
+test = ["importlib-metadata (<5.0)", "mypy (>=1.13)", "pyro-api (>=0.1.1)", "pytest (>=4.1)", "ruff (>=0.1.8)", "scikit-learn", "scipy (>=1.9)"]
+tpu = ["jax[tpu] (>=0.4.25)"]
+
 [[package]]
 name = "oauthlib"
 version = "3.2.2"
 description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
 optional = false
 python-versions = ">=3.6"
+groups = ["executable-dependencies"]
 files = [
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@@ -3776,6 +4074,7 @@ version = "1.6.1"
 description = "The electronic structure package for quantum computers."
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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@@ -3801,6 +4100,7 @@ version = "0.5"
 description = "A plugin allowing OpenFermion to interface with PySCF."
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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@@ -3817,6 +4117,7 @@ version = "0.2.5"
 description = "OpenQAOA is a python open-source multi-backend Software Development Kit to create, customise and execute the Quantum Approximate Optimisation Algorithm (QAOA) on Noisy Intermediate-Scale Quantum (NISQ) devices, and simulators"
 optional = false
 python-versions = ">=3.8, <3.11"
+groups = ["executable-dependencies"]
 files = [
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@@ -3843,28 +4144,23 @@ tests = ["cplex (>=22.1.0.0)", "nbconvert (>=6.5.1)", "pandas (>=1.4.3)", "plotl
 
 [[package]]
 name = "opt-einsum"
-version = "3.3.0"
-description = "Optimizing numpys einsum function"
+version = "3.4.0"
+description = "Path optimization of einsum functions."
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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-[package.dependencies]
-numpy = ">=1.7"
-
-[package.extras]
-docs = ["numpydoc", "sphinx (==1.2.3)", "sphinx-rtd-theme", "sphinxcontrib-napoleon"]
-tests = ["pytest", "pytest-cov", "pytest-pep8"]
-
 [[package]]
 name = "optax"
 version = "0.2.3"
 description = "A gradient processing and optimisation library in JAX."
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
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@@ -3890,6 +4186,7 @@ version = "0.4.4"
 description = "Orbax Checkpoint"
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
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@@ -3917,6 +4214,7 @@ version = "1.0.1"
 description = "Ordered Multivalue Dictionary"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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@@ -3927,76 +4225,157 @@ six = ">=1.8.0"
 
 [[package]]
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-version = "3.10.7"
+version = "3.10.15"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
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 ]
 
+[package.dependencies]
+numpy = ">=1.7"
+qdldl = "*"
+scipy = ">=0.13.2"
+
+[package.extras]
+dev = ["scipy (!=1.12.0)"]
+
 [[package]]
 name = "packaging"
 version = "23.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.7"
+groups = ["base", "executable-dependencies"]
 files = [
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@@ -4008,6 +4387,7 @@ version = "2.2.3"
 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
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@@ -4090,6 +4470,7 @@ version = "0.8.4"
 description = "A Python Parser"
 optional = false
 python-versions = ">=3.6"
+groups = ["executable-dependencies"]
 files = [
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@@ -4105,6 +4486,7 @@ version = "1.4.2"
 description = "Appendable key-value storage"
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
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@@ -4123,6 +4505,7 @@ version = "6.1.0"
 description = "Python Build Reasonableness"
 optional = false
 python-versions = ">=2.6"
+groups = ["executable-dependencies"]
 files = [
     {file = "pbr-6.1.0-py2.py3-none-any.whl", hash = "sha256:a776ae228892d8013649c0aeccbb3d5f99ee15e005a4cbb7e61d55a067b28a2a"},
     {file = "pbr-6.1.0.tar.gz", hash = "sha256:788183e382e3d1d7707db08978239965e8b9e4e5ed42669bf4758186734d5f24"},
@@ -4134,6 +4517,7 @@ version = "1.1.0"
 description = "Package for creating penalty models."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "penaltymodel-1.1.0-py3-none-any.whl", hash = "sha256:f6a5f171a71d14ee1e58c8bbc70adc6575dd56b9ce284d322aa2f4b7345a42a8"},
     {file = "penaltymodel-1.1.0.tar.gz", hash = "sha256:471592e0756a6ca04c3343cb9c2aa525aaaedc42255201826c8ecdd45f592b1e"},
@@ -4148,12 +4532,13 @@ scipy = ">=1.6.0"
 
 [[package]]
 name = "pennylane"
-version = "0.39.0"
+version = "0.40.0"
 description = "PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network."
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
-    {file = "PennyLane-0.39.0-py3-none-any.whl", hash = "sha256:e11928a8ffd652b9c1b4f11955b50210c3b637f36ee3d8cea64a3a9a6a830977"},
+    {file = "PennyLane-0.40.0-py3-none-any.whl", hash = "sha256:c93bb48ff80833edaf424b3b91e01a56cd25b22170bc99b7e53e7168a1ad4dd1"},
 ]
 
 [package.dependencies]
@@ -4161,14 +4546,15 @@ appdirs = "*"
 autograd = "*"
 autoray = ">=0.6.11"
 cachetools = "*"
+diastatic-malt = "*"
 networkx = "*"
 numpy = "<2.1"
 packaging = "*"
-pennylane-lightning = ">=0.39"
+pennylane-lightning = ">=0.40"
 requests = "*"
 rustworkx = ">=0.14.0"
 scipy = "*"
-toml = "*"
+tomlkit = "*"
 typing-extensions = "*"
 
 [package.extras]
@@ -4176,23 +4562,24 @@ kernels = ["cvxopt", "cvxpy"]
 
 [[package]]
 name = "pennylane-catalyst"
-version = "0.9.0"
+version = "0.10.0"
 description = "A JIT compiler for hybrid quantum programs in PennyLane"
 optional = false
 python-versions = ">=3.10"
+groups = ["executable-dependencies"]
 files = [
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+    {file = "PennyLane_Catalyst-0.10.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:744be1cb016b15c9a51bbc1b8ccb7fe4d11526d55b7782b8fc1533aef6c62998"},
+    {file = "PennyLane_Catalyst-0.10.0-cp311-cp311-macosx_13_0_arm64.whl", hash = "sha256:54da27797abd94e54599f0fdcc61e28dac47fef6a2b09b9bc643b1d4b2f43e8a"},
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+    {file = "PennyLane_Catalyst-0.10.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:49090111e111d5a126f82158b47c8b2ef1de4640efd8c86a18dbadbbe6b9e7f8"},
+    {file = "PennyLane_Catalyst-0.10.0-cp312-abi3-macosx_13_0_arm64.whl", hash = "sha256:98e4d88403c907169bde62e03ac71c30779366dfbed821ab55392223330cba95"},
+    {file = "PennyLane_Catalyst-0.10.0-cp312-abi3-macosx_13_0_x86_64.whl", hash = "sha256:5cd40fe9177895acd7ce6533e09ec4591d95450f619c580d4da9041ea66290eb"},
+    {file = "PennyLane_Catalyst-0.10.0-cp312-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:9a1b34b1b922cb91a5da0671b911e3a4c33e03c1269d4c0f36b9a511e19c2d96"},
+    {file = "PennyLane_Catalyst-0.10.0-cp312-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:00f3f255e205a95854c0a83895379f33528aedaaf3b386afd8ac65f300730a03"},
 ]
 
 [package.dependencies]
@@ -4200,19 +4587,19 @@ diastatic-malt = ">=2.15.2"
 jax = "0.4.28"
 jaxlib = "0.4.28"
 numpy = "!=2.0.0"
-pennylane = ">=0.39"
-pennylane-lightning = ">=0.39"
-scipy = "<=1.13"
-tomlkit = {version = "*", markers = "python_version < \"3.11\""}
+pennylane = ">=0.4"
+pennylane-lightning = ">=0.4"
+scipy-openblas32 = ">=0.3.26"
 
 [[package]]
 name = "pennylane-cirq"
-version = "0.39.0"
+version = "0.40.0"
 description = "PennyLane plugin for Cirq"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
-    {file = "PennyLane_Cirq-0.39.0-py3-none-any.whl", hash = "sha256:a66c201dca56a66cf55fbe17dccf9fb495b20973ba402df2a990b693f4bca211"},
+    {file = "PennyLane_Cirq-0.40.0-py3-none-any.whl", hash = "sha256:fecd7e1ac9864efaa5ba4399597418646f71941a513ce311812412a66e450171"},
 ]
 
 [package.dependencies]
@@ -4222,32 +4609,34 @@ pennylane = ">=0.38.0"
 
 [[package]]
 name = "pennylane-lightning"
-version = "0.39.0"
+version = "0.40.0"
 description = "PennyLane-Lightning plugin"
 optional = false
 python-versions = ">=3.10"
-files = [
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-    {file = "PennyLane_Lightning-0.39.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:1878dbf59c779face32ee8b9291e2cf18a601c491a132afe89f9b1e477026455"},
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-    {file = "PennyLane_Lightning-0.39.0.tar.gz", hash = "sha256:a1db6069c7d97eea50846d5b51a3d9ae97539bf68ac480b5c9bea1eaa5e3f1d1"},
+groups = ["executable-dependencies"]
+files = [
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+    {file = "PennyLane_Lightning-0.40.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:a3db3cf2c3caed80ce561b66bb4bf6b5712ecf0b08db986f24c4ff9e187e82b3"},
+    {file = "PennyLane_Lightning-0.40.0-cp311-cp311-win_amd64.whl", hash = "sha256:fed88008b7d468cb1d0e5b3ef92d87f9b600d0896d21eff43655a521cc841d7b"},
+    {file = "PennyLane_Lightning-0.40.0-cp312-cp312-macosx_13_0_arm64.whl", hash = "sha256:e3152f6b53390281334257554ecad90566cd4d200187971d238d2c7691000466"},
+    {file = "PennyLane_Lightning-0.40.0-cp312-cp312-macosx_13_0_x86_64.whl", hash = "sha256:8a4ff3f1d82f664e6d608d155cb019b189aac676c6c7cb40c4f92b15e0d2d485"},
+    {file = "PennyLane_Lightning-0.40.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:b2209727b8c914c8101087da188cbbc7b332bb77ceab4b6791c7ed7b3c5a942c"},
+    {file = "PennyLane_Lightning-0.40.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:2ba97b4d6aa0a6eb774413ea3272d418959b792de6df8e4196171cad450f25af"},
+    {file = "PennyLane_Lightning-0.40.0-cp312-cp312-win_amd64.whl", hash = "sha256:3d7715c84290fc1291e315d2c0d2bf5719cbe86e3e92fb538393ab130b3f5ba2"},
+    {file = "PennyLane_Lightning-0.40.0-py3-none-any.whl", hash = "sha256:96390ce82767b3a66c4f8e0b1bcec2f4f15fb317652cffa9815e371e74458197"},
+    {file = "pennylane_lightning-0.40.0.tar.gz", hash = "sha256:edc95e75ef1b4e6dba96e914893175f9448d4d1b53a6e78614cb7587a8a22f2f"},
 ]
 
 [package.dependencies]
 pennylane = ">=0.37"
+scipy-openblas32 = ">=0.3.26"
 
 [package.extras]
 gpu = ["pennylane-lightning-gpu"]
@@ -4256,19 +4645,20 @@ tensor = ["pennylane-lightning-tensor"]
 
 [[package]]
 name = "pennylane-qiskit"
-version = "0.39.0"
+version = "0.40.0"
 description = "PennyLane plugin for Qiskit"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
-    {file = "PennyLane_qiskit-0.39.0-py3-none-any.whl", hash = "sha256:0551be4022cd72de7ebde7c916778aff119f8e153427c633660250cc131ec38b"},
+    {file = "PennyLane_qiskit-0.40.0-py3-none-any.whl", hash = "sha256:6295926b5acd2e96515459fb7c9dddeadf2c70e2a5f2807c6685fb025c7c4408"},
 ]
 
 [package.dependencies]
 networkx = ">=2.2"
 numpy = "*"
 pennylane = ">=0.38"
-qiskit = ">=0.32"
+qiskit = ">=0.32,<1.3"
 qiskit-aer = "*"
 qiskit-ibm-provider = "*"
 qiskit-ibm-runtime = "<=0.29"
@@ -4276,35 +4666,36 @@ sympy = "<1.13"
 
 [[package]]
 name = "pennylane-qrack"
-version = "0.10.6"
+version = "0.11.1"
 description = "PennyLane plugin for Qrack."
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
-    {file = "pennylane_qrack-0.10.6-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:8ff4fee278a57d7898a345cc5cf9b999de5cc43e4983216fb31fcbcfb97e83a0"},
-    {file = "pennylane_qrack-0.10.6-py3-none-macosx_13_0_x86_64.whl", hash = "sha256:454fd1f2e20ed82b1871b23eefe7ff4ef04f294dfcb29cf751c8cbad8c388bdb"},
-    {file = "pennylane_qrack-0.10.6-py3-none-macosx_14_0_arm64.whl", hash = "sha256:0722c931ed4541e5768791457d168c9be380cbf867c2c0133da32d0a3cd841fa"},
-    {file = "pennylane_qrack-0.10.6-py3-none-macosx_15_0_arm64.whl", hash = "sha256:818d3d17857b4ad16abf1c41d1ead868906b251ed87b46a309a4d343e7510ef7"},
-    {file = "pennylane_qrack-0.10.6-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:470a29c67d186d15784181a3120d426980425c0a8d86d181a4fed067f6cdbb7a"},
-    {file = "pennylane_qrack-0.10.6-py3-none-manylinux_2_35_x86_64.whl", hash = "sha256:a0a7333b2cdfd68e631aefb093b242d2c4674161578131e1e40663baac0f1b8b"},
-    {file = "pennylane_qrack-0.10.6-py3-none-manylinux_2_39_x86_64.whl", hash = "sha256:dfd59d968594b21635817f7b0fa71925b573fa2f72c2024a88c8368d37cc3277"},
-    {file = "pennylane_qrack-0.10.6-py3-none-win_amd64.whl", hash = "sha256:24a600217d7c5d13f1e17506717309dfa57e00df3256851e3956ad0f62999b90"},
-    {file = "pennylane_qrack-0.10.6.tar.gz", hash = "sha256:3ee51483977dbdbf47943bf4be3f85f2a6aa9d556c62eae40e9c8d8cabe5c294"},
+    {file = "pennylane_qrack-0.11.1-py3-none-macosx_13_0_x86_64.whl", hash = "sha256:06d9255430ea5af0081bcb06a2ea9b8d6bb6f9534131b275092e10296f03c2a2"},
+    {file = "pennylane_qrack-0.11.1-py3-none-macosx_14_0_arm64.whl", hash = "sha256:43c2aa4f2819207e93ca0c29f39bcea1720086f3c5eaa02c57668f3e8f0595fa"},
+    {file = "pennylane_qrack-0.11.1-py3-none-macosx_15_0_arm64.whl", hash = "sha256:9e251a85565af5a77538997d94f412f6f86a455ff33f5d308ecf05139e3f2e41"},
+    {file = "pennylane_qrack-0.11.1-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ac0640c4c59877f1f5a9c1be47ef07e949da1d9d1e34547e3d90a6afc01863b2"},
+    {file = "pennylane_qrack-0.11.1-py3-none-manylinux_2_35_x86_64.whl", hash = "sha256:24835ba3287c57f87185a717d3c66a228350f1de24ed44f5bbac0c12d102fdcd"},
+    {file = "pennylane_qrack-0.11.1-py3-none-manylinux_2_39_x86_64.whl", hash = "sha256:4f857ce52add9dc1997b4f0a59357057d5ca8bc21f9e18a1c1e4d1d21edb352f"},
+    {file = "pennylane_qrack-0.11.1-py3-none-win_amd64.whl", hash = "sha256:73fe5dbde0f2acaea768cd9680e68e2752e0b474a7a9982d2b9b6539b651be58"},
+    {file = "pennylane_qrack-0.11.1.tar.gz", hash = "sha256:d8a6124545f1772517c060ffe31e17c3f933e5be0a1006a45a4610daad46c21b"},
 ]
 
 [package.dependencies]
 numpy = ">=1.16"
-pennylane = ">=0.32"
+pennylane = ">=0.39.0"
 pyqrack = ">=1.30.0"
 
 [[package]]
 name = "pennylane-qulacs"
-version = "0.39.0"
+version = "0.40.0"
 description = "PennyLane plugin for Qulacs."
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
-    {file = "pennylane_qulacs-0.39.0-py3-none-any.whl", hash = "sha256:94aee328291f716efe475694137c9055070640c03c9cc9878b5e767f3e4ef982"},
+    {file = "pennylane_qulacs-0.40.0-py3-none-any.whl", hash = "sha256:056341c8877034a32939657ca0b3d746fb8fce5813c61b5377a86d661d0e3959"},
 ]
 
 [package.dependencies]
@@ -4322,6 +4713,7 @@ version = "0.4.0.dev0"
 description = "Sphinx theme for PennyLane open-source Python packages"
 optional = false
 python-versions = "*"
+groups = ["base"]
 files = []
 develop = false
 
@@ -4344,6 +4736,8 @@ version = "4.9.0"
 description = "Pexpect allows easy control of interactive console applications."
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
+markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 fpx = ["olefile"]
 mic = ["olefile"]
-tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
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 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
 [[package]]
 name = "pip"
-version = "24.2"
+version = "24.3.1"
 description = "The PyPA recommended tool for installing Python packages."
 optional = false
 python-versions = ">=3.8"
+groups = ["base"]
 files = [
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 ]
 
 [[package]]
@@ -4466,6 +4853,7 @@ version = "5.24.1"
 description = "An open-source, interactive data visualization library for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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@@ -4481,6 +4869,7 @@ version = "0.4.3"
 description = "Plucking (deep) keys/paths safely from python collections has never been easier."
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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@@ -4492,6 +4881,7 @@ version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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@@ -4507,6 +4897,7 @@ version = "3.11"
 description = "Python Lex & Yacc"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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@@ -4514,27 +4905,121 @@ files = [
 
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 ]
 
 [package.dependencies]
 wcwidth = "*"
 
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 python-versions = ">=3.7"
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 [package.extras]
-test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+dev = ["abi3audit", "black", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest-cov", "requests", "rstcheck", "ruff", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "vulture", "wheel"]
+test = ["pytest", "pytest-xdist", "setuptools"]
 
 [[package]]
 name = "ptyprocess"
@@ -4609,6 +5097,8 @@ version = "0.7.0"
 description = "Run a subprocess in a pseudo terminal"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
+markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""
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 description = "A simple Python wrapper around the PubChem PUG REST API."
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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@@ -4633,6 +5124,7 @@ version = "0.2.3"
 description = "Safely evaluate AST nodes without side effects"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
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 description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
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@@ -4658,6 +5151,7 @@ version = "0.4.1"
 description = "A collection of ASN.1-based protocols modules"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
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 description = "C parser in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
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+typing-extensions = ">=4.12.2"
 
 [package.extras]
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@@ -4699,100 +5196,112 @@ timezone = ["tzdata"]
 
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 [package.dependencies]
@@ -4800,13 +5309,14 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pygments"
-version = "2.18.0"
+version = "2.19.1"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.8"
+groups = ["base", "executable-dependencies"]
 files = [
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 [package.extras]
@@ -4814,13 +5324,14 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pyjwt"
-version = "2.9.0"
+version = "2.10.1"
 description = "JSON Web Token implementation in Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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 files = [
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 ]
 
 [package.extras]
@@ -4835,6 +5346,7 @@ version = "1.5"
 description = "Thin wrapper for pandoc."
 optional = false
 python-versions = "*"
+groups = ["base"]
 files = [
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@@ -4851,6 +5363,7 @@ version = "3.0.9"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.6.8"
+groups = ["executable-dependencies"]
 files = [
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@@ -4865,6 +5378,7 @@ version = "1.9.0"
 description = "A cross-platform clipboard module for Python. (Only handles plain text for now.)"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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 ]
@@ -4875,6 +5389,7 @@ version = "1.32.12"
 description = "pyqrack - Pure Python vm6502q/qrack Wrapper"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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@@ -4896,6 +5411,7 @@ version = "4.10.1"
 description = "A Python library for creating Quantum Instruction Language (Quil) programs."
 optional = false
 python-versions = "<=3.12,>=3.8"
+groups = ["executable-dependencies"]
 files = [
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@@ -4926,6 +5442,7 @@ version = "0.20.0"
 description = "Persistent/Functional/Immutable data structures"
 optional = false
 python-versions = ">=3.8"
+groups = ["metadata-validation"]
 files = [
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@@ -4963,27 +5480,29 @@ files = [
 
 [[package]]
 name = "pyscf"
-version = "2.6.2"
+version = "2.8.0"
 description = "PySCF: Python-based Simulations of Chemistry Framework"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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 ]
 
 [package.dependencies]
 h5py = ">=2.7"
 numpy = ">=1.13,<1.16 || >1.16,<1.17 || >1.17"
-scipy = "<1.5.0 || >1.5.0,<1.5.1 || >1.5.1"
+scipy = ">=1.6.0"
 setuptools = "*"
 
 [package.extras]
-all = ["pyscf[bse,cppe,dispersion,doci,forge,geomopt,properties,pyqmc,semiempirical]"]
+all = ["pyscf[bse,ccpy,cppe,dispersion,doci,forge,geomopt,properties,pyqmc,semiempirical]"]
 bse = ["basis-set-exchange"]
+ccpy = ["coupled-cluster-py"]
 cornell-shci = ["pyscf-cornell-shci"]
 cppe = ["cppe"]
 dispersion = ["pyscf-dispersion"]
@@ -5005,6 +5524,7 @@ version = "1.7.1"
 description = "A Python SOCKS client module. See https://github.com/Anorov/PySocks for more information."
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["executable-dependencies"]
 files = [
     {file = "PySocks-1.7.1-py27-none-any.whl", hash = "sha256:08e69f092cc6dbe92a0fdd16eeb9b9ffbc13cadfe5ca4c7bd92ffb078b293299"},
     {file = "PySocks-1.7.1-py3-none-any.whl", hash = "sha256:2725bd0a9925919b9b51739eea5f9e2bae91e83288108a9ad338b2e3a4435ee5"},
@@ -5013,13 +5533,14 @@ files = [
 
 [[package]]
 name = "pyspnego"
-version = "0.11.1"
+version = "0.11.2"
 description = "Windows Negotiate Authentication Client and Server"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
-    {file = "pyspnego-0.11.1-py3-none-any.whl", hash = "sha256:129a4294f2c4d681d5875240ef87accc6f1d921e8983737fb0b59642b397951e"},
-    {file = "pyspnego-0.11.1.tar.gz", hash = "sha256:e92ed8b0a62765b9d6abbb86a48cf871228ddb97678598dc01c9c39a626823f6"},
+    {file = "pyspnego-0.11.2-py3-none-any.whl", hash = "sha256:74abc1fb51e59360eb5c5c9086e5962174f1072c7a50cf6da0bda9a4bcfdfbd4"},
+    {file = "pyspnego-0.11.2.tar.gz", hash = "sha256:994388d308fb06e4498365ce78d222bf4f3570b6df4ec95738431f61510c971b"},
 ]
 
 [package.dependencies]
@@ -5032,13 +5553,14 @@ yaml = ["ruamel.yaml"]
 
 [[package]]
 name = "pytest"
-version = "8.3.3"
+version = "8.3.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
-    {file = "pytest-8.3.3-py3-none-any.whl", hash = "sha256:a6853c7375b2663155079443d2e45de913a911a11d669df02a50814944db57b2"},
-    {file = "pytest-8.3.3.tar.gz", hash = "sha256:70b98107bd648308a7952b06e6ca9a50bc660be218d53c257cc1fc94fda10181"},
+    {file = "pytest-8.3.4-py3-none-any.whl", hash = "sha256:50e16d954148559c9a74109af1eaf0c945ba2d8f30f0a3d3335edde19788b6f6"},
+    {file = "pytest-8.3.4.tar.gz", hash = "sha256:965370d062bce11e73868e0335abac31b4d3de0e82f4007408d242b4f8610761"},
 ]
 
 [package.dependencies]
@@ -5058,6 +5580,7 @@ version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["executable-dependencies", "metadata-validation"]
 files = [
     {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
     {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
@@ -5072,6 +5595,7 @@ version = "1.0.1"
 description = "Read key-value pairs from a .env file and set them as environment variables"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "python-dotenv-1.0.1.tar.gz", hash = "sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca"},
     {file = "python_dotenv-1.0.1-py3-none-any.whl", hash = "sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a"},
@@ -5082,13 +5606,14 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "python-engineio"
-version = "4.9.1"
+version = "4.11.2"
 description = "Engine.IO server and client for Python"
 optional = false
 python-versions = ">=3.6"
+groups = ["executable-dependencies"]
 files = [
-    {file = "python_engineio-4.9.1-py3-none-any.whl", hash = "sha256:f995e702b21f6b9ebde4e2000cd2ad0112ba0e5116ec8d22fe3515e76ba9dddd"},
-    {file = "python_engineio-4.9.1.tar.gz", hash = "sha256:7631cf5563086076611e494c643b3fa93dd3a854634b5488be0bba0ef9b99709"},
+    {file = "python_engineio-4.11.2-py3-none-any.whl", hash = "sha256:f0971ac4c65accc489154fe12efd88f53ca8caf04754c46a66e85f5102ef22ad"},
+    {file = "python_engineio-4.11.2.tar.gz", hash = "sha256:145bb0daceb904b4bb2d3eb2d93f7dbb7bb87a6a0c4f20a94cc8654dec977129"},
 ]
 
 [package.dependencies]
@@ -5105,6 +5630,7 @@ version = "1.20"
 description = "Python wrapper around rapidjson"
 optional = false
 python-versions = ">=3.6"
+groups = ["executable-dependencies"]
 files = [
     {file = "python_rapidjson-1.20-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:eeaa8487fdd8db409bd2e0c41c59cee3b9f1d08401fc75520f7d35c7a22d8789"},
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@@ -5183,18 +5709,19 @@ files = [
 
 [[package]]
 name = "python-socketio"
-version = "5.11.4"
+version = "5.12.1"
 description = "Socket.IO server and client for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
-    {file = "python_socketio-5.11.4-py3-none-any.whl", hash = "sha256:42efaa3e3e0b166fc72a527488a13caaac2cefc76174252486503bd496284945"},
-    {file = "python_socketio-5.11.4.tar.gz", hash = "sha256:8b0b8ff2964b2957c865835e936310190639c00310a47d77321a594d1665355e"},
+    {file = "python_socketio-5.12.1-py3-none-any.whl", hash = "sha256:24a0ea7cfff0e021eb28c68edbf7914ee4111bdf030b95e4d250c4dc9af7a386"},
+    {file = "python_socketio-5.12.1.tar.gz", hash = "sha256:0299ff1f470b676c09c1bfab1dead25405077d227b2c13cf217a34dadc68ba9c"},
 ]
 
 [package.dependencies]
 bidict = ">=0.21.0"
-python-engineio = ">=4.8.0"
+python-engineio = ">=4.11.0"
 
 [package.extras]
 asyncio-client = ["aiohttp (>=3.4)"]
@@ -5207,6 +5734,7 @@ version = "2024.2"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
     {file = "pytz-2024.2-py2.py3-none-any.whl", hash = "sha256:31c7c1817eb7fae7ca4b8c7ee50c72f93aa2dd863de768e1ef4245d426aa0725"},
     {file = "pytz-2024.2.tar.gz", hash = "sha256:2aa355083c50a0f93fa581709deac0c9ad65cca8a9e9beac660adcbd493c798a"},
@@ -5218,6 +5746,7 @@ version = "6.0.2"
 description = "YAML parser and emitter for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["base", "executable-dependencies"]
 files = [
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086"},
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:29717114e51c84ddfba879543fb232a6ed60086602313ca38cce623c1d62cfbf"},
@@ -5280,6 +5809,7 @@ version = "26.2.0"
 description = "Python bindings for 0MQ"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "pyzmq-26.2.0-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:ddf33d97d2f52d89f6e6e7ae66ee35a4d9ca6f36eda89c24591b0c40205a3629"},
     {file = "pyzmq-26.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:dacd995031a01d16eec825bf30802fceb2c3791ef24bcce48fa98ce40918c27b"},
@@ -5401,6 +5931,7 @@ version = "0.7.0"
 description = "Library for quantum circuit rewriting and optimisation using the ZX-calculus"
 optional = false
 python-versions = ">=3.6"
+groups = ["executable-dependencies"]
 files = [
     {file = "pyzx-0.7.0-py3-none-any.whl", hash = "sha256:9a06c0547abd2d753767521f95d5e17fbc6b08eb8de8707b6424e103b803e7c4"},
     {file = "pyzx-0.7.0.tar.gz", hash = "sha256:3630ccab6df6818420efd2d4e60bb0bc8ce0461471a16071df30b4d2646074b3"},
@@ -5418,6 +5949,7 @@ version = "0.17.9"
 description = "Python interface for the QCS Rust SDK"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
     {file = "qcs_sdk_python-0.17.9-cp310-cp310-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:5797c1db68b4194f0729fe56e93a7d25dbcf752989fbf26d9d6709d020e8d367"},
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@@ -5445,24 +5977,78 @@ files = [
 [package.dependencies]
 quil = "0.10.0"
 
+[[package]]
+name = "qdldl"
+version = "0.1.7.post5"
+description = "QDLDL, a free LDL factorization routine."
+optional = false
+python-versions = "*"
+groups = ["executable-dependencies"]
+files = [
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+[package.dependencies]
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+scipy = ">=0.13.2"
+
 [[package]]
 name = "qiskit"
-version = "1.2.2"
+version = "1.2.4"
 description = "An open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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 [package.dependencies]
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 rustworkx = ">=0.15.0"
 scipy = ">=1.5"
 stevedore = ">=3.0.0"
-symengine = ">=0.11"
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 sympy = ">=1.3"
 typing-extensions = "*"
 
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 ]
 
 [package.dependencies]
@@ -5544,6 +6131,7 @@ version = "0.11.0"
 description = "Qiskit IBM Quantum Provider for accessing the quantum devices and simulators at IBM"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "qiskit_ibm_provider-0.11.0-py3-none-any.whl", hash = "sha256:3d84aff2df00d0642a9fcb53cf7f7442e62d65d3bbe13e191bc078b2ee474dc6"},
     {file = "qiskit_ibm_provider-0.11.0.tar.gz", hash = "sha256:31a29701120dd0b27e1d8e691b4e7262580cda6f85b85610697577920d8c2f0c"},
@@ -5569,6 +6157,7 @@ version = "0.29.0"
 description = "IBM Quantum client for Qiskit Runtime."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "qiskit_ibm_runtime-0.29.0-py3-none-any.whl", hash = "sha256:efdf7116fddea966f2811237f6acb612293b1f87a8baea231ac483d486f05276"},
     {file = "qiskit_ibm_runtime-0.29.0.tar.gz", hash = "sha256:bed95faa8275930f791840be64e909f6a10835865db0bbbc16e8f4e7d6ac008e"},
@@ -5591,6 +6180,7 @@ version = "0.10.0"
 description = "A Python package for building and parsing Quil programs."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "quil-0.10.0-cp310-cp310-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:3343ac253a6d8f65c0f171c381faab1b00cb18122d4adc1d559e757d5bae84ce"},
     {file = "quil-0.10.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b9a21333d37990fd627b270b48d864a1497171f05b2e8264747270a9912eb06e"},
@@ -5626,6 +6216,7 @@ version = "1.8.2"
 description = "Quantum information and many-body library."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "quimb-1.8.2-py3-none-any.whl", hash = "sha256:d1a3412cec0dc0df3d9c942d7331d3c5947bce79434b73fd11cca3ee51eea855"},
     {file = "quimb-1.8.2.tar.gz", hash = "sha256:f3529770da6b5283f072d8ff41b990b41a418563546376bc489b6ccc7102bec7"},
@@ -5653,6 +6244,8 @@ version = "0.6.1"
 description = "Quantum circuit simulator for research"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
+markers = "platform_machine == \"x86_64\""
 files = [
     {file = "qulacs-0.6.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f1f1951e9cb55e11ded47b7a652bb03b06f986ba19e6c2ea76b40b159f297f62"},
     {file = "qulacs-0.6.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:7b922972a7b9a827c16dc9e2c14478f276ead2eacad7d545db03ab795854b94f"},
@@ -5692,6 +6285,7 @@ version = "4.7.3"
 description = "QuTiP: The Quantum Toolbox in Python"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
     {file = "qutip-4.7.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ea947c32b5b31446997113271891fe2af91b9e7c58fca92482ce816de66fcbbc"},
     {file = "qutip-4.7.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d3534b7b7d33c1536c0fe3bc000c4ef6ba33a9bca327234d520a982a06ad07b3"},
@@ -5737,6 +6331,7 @@ version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
+groups = ["base", "executable-dependencies", "metadata-validation"]
 files = [
     {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
     {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
@@ -5759,6 +6354,7 @@ version = "1.3.0"
 description = "This package allows for HTTP NTLM authentication using the requests library."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "requests_ntlm-1.3.0-py3-none-any.whl", hash = "sha256:4c7534a7d0e482bb0928531d621be4b2c74ace437e88c5a357ceb7452d25a510"},
     {file = "requests_ntlm-1.3.0.tar.gz", hash = "sha256:b29cc2462623dffdf9b88c43e180ccb735b4007228a542220e882c58ae56c668"},
@@ -5775,6 +6371,7 @@ version = "2.0.0"
 description = "OAuthlib authentication support for Requests."
 optional = false
 python-versions = ">=3.4"
+groups = ["executable-dependencies"]
 files = [
     {file = "requests-oauthlib-2.0.0.tar.gz", hash = "sha256:b3dffaebd884d8cd778494369603a9e7b58d29111bf6b41bdc2dcd87203af4e9"},
     {file = "requests_oauthlib-2.0.0-py2.py3-none-any.whl", hash = "sha256:7dd8a5c40426b779b0868c404bdef9768deccf22749cde15852df527e6269b36"},
@@ -5793,6 +6390,8 @@ version = "0.15.1"
 description = "A python graph library implemented in Rust"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
+markers = "platform_machine == \"x86_64\""
 files = [
     {file = "retworkx-0.15.1-py3-none-any.whl", hash = "sha256:1e9f106b915215d57eb209076df3a07a6e34a258c98efe48bdae69bbd0e07282"},
 ]
@@ -5812,6 +6411,7 @@ version = "0.1.4"
 description = "A pure python RFC3339 validator"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["metadata-validation"]
 files = [
     {file = "rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa"},
     {file = "rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b"},
@@ -5826,6 +6426,7 @@ version = "1.3.8"
 description = "Parsing and validation of URIs (RFC 3986) and IRIs (RFC 3987)"
 optional = false
 python-versions = "*"
+groups = ["metadata-validation"]
 files = [
     {file = "rfc3987-1.3.8-py2.py3-none-any.whl", hash = "sha256:10702b1e51e5658843460b189b185c0366d2cf4cff716f13111b0ea9fd2dce53"},
     {file = "rfc3987-1.3.8.tar.gz", hash = "sha256:d3c4d257a560d544e9826b38bc81db676890c79ab9d7ac92b39c7a253d5ca733"},
@@ -5837,6 +6438,7 @@ version = "13.3.5"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 optional = false
 python-versions = ">=3.7.0"
+groups = ["executable-dependencies"]
 files = [
     {file = "rich-13.3.5-py3-none-any.whl", hash = "sha256:69cdf53799e63f38b95b9bf9c875f8c90e78dd62b2f00c13a911c7a3b9fa4704"},
     {file = "rich-13.3.5.tar.gz", hash = "sha256:2d11b9b8dd03868f09b4fffadc84a6a8cda574e40dc90821bd845720ebb8e89c"},
@@ -5855,6 +6457,7 @@ version = "3.11.0"
 description = "The RPC framework and message specification for Rigetti QCS."
 optional = false
 python-versions = ">=3.6"
+groups = ["executable-dependencies"]
 files = [
     {file = "rpcq-3.11.0.tar.gz", hash = "sha256:4361e759782f58dd0b8aa3a6d901e3ea5709f91c6a060bd444081fbb007b05a9"},
 ]
@@ -5871,6 +6474,7 @@ version = "4.9"
 description = "Pure-Python RSA implementation"
 optional = false
 python-versions = ">=3.6,<4"
+groups = ["executable-dependencies"]
 files = [
     {file = "rsa-4.9-py3-none-any.whl", hash = "sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7"},
     {file = "rsa-4.9.tar.gz", hash = "sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21"},
@@ -5885,6 +6489,7 @@ version = "0.17.32"
 description = "ruamel.yaml is a YAML parser/emitter that supports roundtrip preservation of comments, seq/map flow style, and map key order"
 optional = false
 python-versions = ">=3"
+groups = ["executable-dependencies", "metadata-validation"]
 files = [
     {file = "ruamel.yaml-0.17.32-py3-none-any.whl", hash = "sha256:23cd2ed620231677564646b0c6a89d138b6822a0d78656df7abda5879ec4f447"},
     {file = "ruamel.yaml-0.17.32.tar.gz", hash = "sha256:ec939063761914e14542972a5cba6d33c23b0859ab6342f61cf070cfc600efc2"},
@@ -5899,61 +6504,59 @@ jinja2 = ["ruamel.yaml.jinja2 (>=0.2)"]
 
 [[package]]
 name = "ruamel-yaml-clib"
-version = "0.2.8"
+version = "0.2.12"
 description = "C version of reader, parser and emitter for ruamel.yaml derived from libyaml"
 optional = false
-python-versions = ">=3.6"
-files = [
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@@ -5962,6 +6565,7 @@ version = "0.15.1"
 description = "A python graph library implemented in Rust"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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@@ -5991,6 +6595,8 @@ version = "1.3.0"
 description = "A set of python modules for machine learning and data mining"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
+markers = "platform_machine == \"x86_64\""
 files = [
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@@ -6033,6 +6639,7 @@ version = "1.11.4"
 description = "Fundamental algorithms for scientific computing in Python"
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
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@@ -6069,12 +6676,88 @@ dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyl
 doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
 test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
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+]
+
+[package.dependencies]
+numpy = "*"
+scipy = "*"
+
 [[package]]
 name = "seaborn"
 version = "0.13.2"
 description = "Statistical data visualization"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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@@ -6096,6 +6779,7 @@ version = "2.10.0"
 description = "A library implementing the 'SemVer' scheme."
 optional = false
 python-versions = ">=2.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "semantic_version-2.10.0-py2.py3-none-any.whl", hash = "sha256:de78a3b8e0feda74cabc54aab2da702113e33ac9d9eb9d2389bcf1f58b7d9177"},
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@@ -6107,39 +6791,42 @@ doc = ["Sphinx", "sphinx-rtd-theme"]
 
 [[package]]
 name = "setuptools"
-version = "75.1.0"
+version = "75.8.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
+groups = ["base", "executable-dependencies"]
 files = [
-    {file = "setuptools-75.1.0-py3-none-any.whl", hash = "sha256:35ab7fd3bcd95e6b7fd704e4a1539513edad446c097797f2985e0e4b960772f2"},
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+    {file = "setuptools-75.8.0-py3-none-any.whl", hash = "sha256:e3982f444617239225d675215d51f6ba05f845d4eec313da4418fdbb56fb27e3"},
+    {file = "setuptools-75.8.0.tar.gz", hash = "sha256:c5afc8f407c626b8313a86e10311dd3f661c6cd9c09d4bf8c15c0e11f9f2b0e6"},
 ]
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.5.2)"]
-core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.collections", "jaraco.functools", "jaraco.text (>=3.7)", "more-itertools", "more-itertools (>=8.8)", "packaging", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.8.0)"]
+core = ["importlib_metadata (>=6)", "jaraco.collections", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 enabler = ["pytest-enabler (>=2.2)"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
-type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.11.*)", "pytest-mypy"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.14.*)", "pytest-mypy"]
 
 [[package]]
 name = "simple-websocket"
-version = "1.0.0"
+version = "1.1.0"
 description = "Simple WebSocket server and client for Python"
 optional = false
 python-versions = ">=3.6"
+groups = ["executable-dependencies"]
 files = [
-    {file = "simple-websocket-1.0.0.tar.gz", hash = "sha256:17d2c72f4a2bd85174a97e3e4c88b01c40c3f81b7b648b0cc3ce1305968928c8"},
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+    {file = "simple_websocket-1.1.0-py3-none-any.whl", hash = "sha256:4af6069630a38ed6c561010f0e11a5bc0d4ca569b36306eb257cd9a192497c8c"},
+    {file = "simple_websocket-1.1.0.tar.gz", hash = "sha256:7939234e7aa067c534abdab3a9ed933ec9ce4691b0713c78acb195560aa52ae4"},
 ]
 
 [package.dependencies]
 wsproto = "*"
 
 [package.extras]
+dev = ["flake8", "pytest", "pytest-cov", "tox"]
 docs = ["sphinx"]
 
 [[package]]
@@ -6148,6 +6835,7 @@ version = "3.19.3"
 description = "Simple, fast, extensible JSON encoder/decoder for Python"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.5"
+groups = ["executable-dependencies"]
 files = [
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@@ -6263,13 +6951,14 @@ files = [
 
 [[package]]
 name = "six"
-version = "1.16.0"
+version = "1.17.0"
 description = "Python 2 and 3 compatibility utilities"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["executable-dependencies", "metadata-validation"]
 files = [
-    {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
-    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+    {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"},
+    {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"},
 ]
 
 [[package]]
@@ -6278,6 +6967,7 @@ version = "1.3.1"
 description = "Sniff out which async library your code is running under"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
     {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
@@ -6289,6 +6979,7 @@ version = "2.2.0"
 description = "This package provides 29 stemmers for 28 languages generated from Snowball algorithms."
 optional = false
 python-versions = "*"
+groups = ["base", "executable-dependencies"]
 files = [
     {file = "snowballstemmer-2.2.0-py2.py3-none-any.whl", hash = "sha256:c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a"},
     {file = "snowballstemmer-2.2.0.tar.gz", hash = "sha256:09b16deb8547d3412ad7b590689584cd0fe25ec8db3be37788be3810cbf19cb1"},
@@ -6300,6 +6991,7 @@ version = "2.4.0"
 description = "Sorted Containers -- Sorted List, Sorted Dict, Sorted Set"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
     {file = "sortedcontainers-2.4.0-py2.py3-none-any.whl", hash = "sha256:a163dcaede0f1c021485e957a39245190e74249897e2ae4b2aa38595db237ee0"},
     {file = "sortedcontainers-2.4.0.tar.gz", hash = "sha256:25caa5a06cc30b6b83d11423433f65d1f9d76c4c6a0c90e3379eaa43b9bfdb88"},
@@ -6311,6 +7003,7 @@ version = "5.3.0"
 description = "Python documentation generator"
 optional = false
 python-versions = ">=3.6"
+groups = ["base", "executable-dependencies"]
 files = [
     {file = "Sphinx-5.3.0.tar.gz", hash = "sha256:51026de0a9ff9fc13c05d74913ad66047e104f56a129ff73e174eb5c3ee794b5"},
     {file = "sphinx-5.3.0-py3-none-any.whl", hash = "sha256:060ca5c9f7ba57a08a1219e547b269fadf125ae25b06b9fa7f66768efb652d6d"},
@@ -6345,6 +7038,7 @@ version = "0.17.1"
 description = "A Sphinx extension that builds an HTML gallery of examples from any set of Python scripts."
 optional = false
 python-versions = ">=3.8"
+groups = ["base", "executable-dependencies"]
 files = [
     {file = "sphinx_gallery-0.17.1-py3-none-any.whl", hash = "sha256:0a1142a15a9d63169fe7b12167dc028891fb8db31bfc6d7de03ba0d68d591830"},
     {file = "sphinx_gallery-0.17.1.tar.gz", hash = "sha256:c9969abcc5ca8c24496014da8260833b8c3ccdb32c17716b5ba66f2e0a3cc183"},
@@ -6369,6 +7063,7 @@ version = "1.0.8"
 description = "sphinxcontrib-applehelp is a Sphinx extension which outputs Apple help books"
 optional = false
 python-versions = ">=3.9"
+groups = ["base", "executable-dependencies"]
 files = [
     {file = "sphinxcontrib_applehelp-1.0.8-py3-none-any.whl", hash = "sha256:cb61eb0ec1b61f349e5cc36b2028e9e7ca765be05e49641c97241274753067b4"},
     {file = "sphinxcontrib_applehelp-1.0.8.tar.gz", hash = "sha256:c40a4f96f3776c4393d933412053962fac2b84f4c99a7982ba42e09576a70619"},
@@ -6385,6 +7080,7 @@ version = "2.0.0"
 description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp documents"
 optional = false
 python-versions = ">=3.9"
+groups = ["base", "executable-dependencies"]
 files = [
     {file = "sphinxcontrib_devhelp-2.0.0-py3-none-any.whl", hash = "sha256:aefb8b83854e4b0998877524d1029fd3e6879210422ee3780459e28a1f03a8a2"},
     {file = "sphinxcontrib_devhelp-2.0.0.tar.gz", hash = "sha256:411f5d96d445d1d73bb5d52133377b4248ec79db5c793ce7dbe59e074b4dd1ad"},
@@ -6401,6 +7097,7 @@ version = "2.1.0"
 description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files"
 optional = false
 python-versions = ">=3.9"
+groups = ["base", "executable-dependencies"]
 files = [
     {file = "sphinxcontrib_htmlhelp-2.1.0-py3-none-any.whl", hash = "sha256:166759820b47002d22914d64a075ce08f4c46818e17cfc9470a9786b759b19f8"},
     {file = "sphinxcontrib_htmlhelp-2.1.0.tar.gz", hash = "sha256:c9e2916ace8aad64cc13a0d233ee22317f2b9025b9cf3295249fa985cc7082e9"},
@@ -6417,6 +7114,7 @@ version = "1.0.1"
 description = "A sphinx extension which renders display math in HTML via JavaScript"
 optional = false
 python-versions = ">=3.5"
+groups = ["base", "executable-dependencies"]
 files = [
     {file = "sphinxcontrib-jsmath-1.0.1.tar.gz", hash = "sha256:a9925e4a4587247ed2191a22df5f6970656cb8ca2bd6284309578f2153e0c4b8"},
     {file = "sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl", hash = "sha256:2ec2eaebfb78f3f2078e73666b1415417a116cc848b72e5172e596c871103178"},
@@ -6431,6 +7129,7 @@ version = "2.0.0"
 description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp documents"
 optional = false
 python-versions = ">=3.9"
+groups = ["base", "executable-dependencies"]
 files = [
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 ]
 
 [[package]]
@@ -6607,6 +7317,7 @@ version = "0.6.3"
 description = "Extract data from python stack frames and tracebacks for informative displays"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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@@ -6622,13 +7333,14 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "starlette"
-version = "0.38.6"
+version = "0.41.3"
 description = "The little ASGI library that shines."
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -6639,13 +7351,14 @@ full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7
 
 [[package]]
 name = "stevedore"
-version = "5.3.0"
+version = "5.4.0"
 description = "Manage dynamic plugins for Python applications"
 optional = false
-python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -6657,6 +7370,7 @@ version = "1.14.0"
 description = "A fast library for analyzing with quantum stabilizer circuits."
 optional = false
 python-versions = ">=3.6.0"
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@@ -6696,46 +7410,54 @@ numpy = "*"
 
 [[package]]
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 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
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 description = "Pretty-print tabular data"
 optional = false
 python-versions = ">=3.7"
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 description = "Traceback serialization library."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
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@@ -6783,6 +7508,7 @@ version = "8.5.0"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
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@@ -6798,6 +7524,7 @@ version = "2.14.1"
 description = "TensorBoard lets you watch Tensors Flow"
 optional = false
 python-versions = ">=3.9"
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 files = [
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@@ -6822,6 +7549,7 @@ version = "0.7.2"
 description = "Fast data loading for TensorBoard"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
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@@ -6834,6 +7562,8 @@ version = "2.14.1"
 description = "TensorFlow is an open source machine learning framework for everyone."
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
+markers = "sys_platform != \"darwin\" or platform_machine != \"arm64\""
 files = [
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@@ -6885,6 +7615,7 @@ version = "2.14.0"
 description = "TensorFlow Estimator."
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "tensorflow_estimator-2.14.0-py2.py3-none-any.whl", hash = "sha256:820bf57c24aa631abb1bbe4371739ed77edb11361d61381fd8e790115ac0fd57"},
 ]
@@ -6895,6 +7626,7 @@ version = "0.37.1"
 description = "TensorFlow IO"
 optional = false
 python-versions = "<3.13,>=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "tensorflow_io_gcs_filesystem-0.37.1-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:249c12b830165841411ba71e08215d0e94277a49c551e6dd5d72aab54fe5491b"},
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@@ -6927,6 +7659,8 @@ version = "2.14.1"
 description = "TensorFlow is an open source machine learning framework for everyone."
 optional = false
 python-versions = ">=3.9"
+groups = ["executable-dependencies"]
+markers = "sys_platform == \"darwin\" and platform_machine == \"arm64\""
 files = [
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@@ -6966,6 +7700,7 @@ version = "0.1.45"
 description = "Read and write large, multi-dimensional arrays"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "tensorstore-0.1.45-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:2ff6e5177ba2702f348bef3edc37619aa7646e43f33d1a567ba267db455699e4"},
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@@ -6991,13 +7726,14 @@ numpy = ">=1.16.0"
 
 [[package]]
 name = "termcolor"
-version = "2.4.0"
+version = "2.5.0"
 description = "ANSI color formatting for output in terminal"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
+groups = ["executable-dependencies"]
 files = [
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 ]
 
 [package.extras]
@@ -7009,6 +7745,7 @@ version = "0.3.6"
 description = "TF2JAX: Convert TensorFlow to JAX"
 optional = false
 python-versions = ">=3.7"
+groups = ["executable-dependencies"]
 files = [
     {file = "tf2jax-0.3.6-py3-none-any.whl", hash = "sha256:42c17bdefed405dfb3b87c3fe14107ee356a3a95e8637959f377036764488782"},
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@@ -7029,6 +7766,8 @@ version = "0.21.0"
 description = "Open source library for hafnian calculation"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
+markers = "platform_machine == \"x86_64\""
 files = [
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@@ -7047,6 +7786,8 @@ version = "3.5.0"
 description = "threadpoolctl"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
+markers = "platform_machine == \"x86_64\""
 files = [
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@@ -7058,6 +7799,7 @@ version = "0.10.2"
 description = "Python Library for Tom's Obvious, Minimal Language"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+groups = ["executable-dependencies"]
 files = [
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@@ -7065,13 +7807,44 @@ files = [
 
 [[package]]
 name = "tomli"
-version = "2.0.1"
+version = "2.2.1"
 description = "A lil' TOML parser"
 optional = false
-python-versions = ">=3.7"
-files = [
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-    {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
+python-versions = ">=3.8"
+groups = ["executable-dependencies"]
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+    {file = "tomli-2.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:a38aa0308e754b0e3c67e344754dff64999ff9b513e691d0e786265c93583c69"},
+    {file = "tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc"},
+    {file = "tomli-2.2.1.tar.gz", hash = "sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff"},
 ]
 
 [[package]]
@@ -7080,6 +7853,7 @@ version = "0.13.2"
 description = "Style preserving TOML library"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
     {file = "tomlkit-0.13.2-py3-none-any.whl", hash = "sha256:7a974427f6e119197f670fbbbeae7bef749a6c14e793db934baefc1b5f03efde"},
     {file = "tomlkit-0.13.2.tar.gz", hash = "sha256:fff5fe59a87295b278abd31bec92c15d9bc4a06885ab12bcea52c71119392e79"},
@@ -7087,13 +7861,14 @@ files = [
 
 [[package]]
 name = "toolz"
-version = "0.12.1"
+version = "1.0.0"
 description = "List processing tools and functional utilities"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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+    {file = "toolz-1.0.0-py3-none-any.whl", hash = "sha256:292c8f1c4e7516bf9086f8850935c799a874039c8bcf959d47b600e4c44a6236"},
+    {file = "toolz-1.0.0.tar.gz", hash = "sha256:2c86e3d9a04798ac556793bced838816296a2f085017664e4995cb40a1047a02"},
 ]
 
 [[package]]
@@ -7102,6 +7877,8 @@ version = "2.1.2"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
+groups = ["executable-dependencies"]
+markers = "sys_platform == \"darwin\" and platform_machine == \"arm64\""
 files = [
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@@ -7143,6 +7920,8 @@ version = "2.1.2+cpu"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
+groups = ["executable-dependencies"]
+markers = "platform_machine == \"x86_64\""
 files = [
     {file = "torch-2.1.2+cpu-cp310-cp310-linux_x86_64.whl", hash = "sha256:bf3ca897f8c7c218dd6c4b1cc5eec57b4f4e71106b0b8120e92f5fdaf4acf6cd"},
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@@ -7177,6 +7956,8 @@ version = "0.16.2"
 description = "image and video datasets and models for torch deep learning"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
+markers = "sys_platform == \"darwin\" and platform_machine == \"arm64\""
 files = [
     {file = "torchvision-0.16.2-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:bc86f2800cb2c0c1a09c581409cdd6bff66e62f103dc83fc63f73346264c3756"},
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@@ -7215,6 +7996,8 @@ version = "0.16.2+cpu"
 description = "image and video datasets and models for torch deep learning"
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
+markers = "platform_machine == \"x86_64\""
 files = [
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@@ -7242,40 +8025,43 @@ reference = "pytorch-cpu"
 
 [[package]]
 name = "tornado"
-version = "6.4.1"
+version = "6.4.2"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
 python-versions = ">=3.8"
+groups = ["executable-dependencies"]
 files = [
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 python-versions = ">=3.7"
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 [package.extras]
-dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
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+discord = ["requests"]
 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
@@ -7286,6 +8072,7 @@ version = "5.14.3"
 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
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 [[package]]
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 optional = false
 python-versions = ">=3.8"
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 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
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 description = "RFC 6570 URI Template Processor"
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 python-versions = ">=3.7"
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 python-versions = ">=3.7"
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 [package.dependencies]
@@ -7593,6 +8387,7 @@ version = "0.2.13"
 description = "Measures the displayed width of unicode strings in a terminal"
 optional = false
 python-versions = "*"
+groups = ["executable-dependencies"]
 files = [
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+version = "24.11.1"
 description = "A library for working with the color formats defined by HTML and CSS."
 optional = false
-python-versions = ">=3.8"
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-[package.extras]
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-tests = ["coverage[toml]"]
-
 [[package]]
 name = "websocket-client"
 version = "1.8.0"
 description = "WebSocket client for Python with low level API options"
 optional = false
 python-versions = ">=3.8"
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-version = "3.0.4"
+version = "3.1.3"
 description = "The comprehensive WSGI web application library."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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 [package.dependencies]
@@ -7743,13 +8521,14 @@ watchdog = ["watchdog (>=2.3)"]
 
 [[package]]
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-version = "0.44.0"
+version = "0.45.1"
 description = "A built-package format for Python"
 optional = false
 python-versions = ">=3.8"
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 description = "Module for decorators, wrappers and monkey patching."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
+groups = ["executable-dependencies"]
 files = [
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 description = "WebSockets state-machine based protocol implementation"
 optional = false
 python-versions = ">=3.7.0"
+groups = ["executable-dependencies"]
 files = [
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 description = "Sphinx theme for Xanadu open-source Python packages"
 optional = false
 python-versions = "*"
+groups = ["base"]
 files = [
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@@ -7871,108 +8653,100 @@ sphinx-gallery = "*"
 
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 ]
 
 [metadata]
-lock-version = "2.0"
+lock-version = "2.1"
 python-versions = "~3.10.0"
-content-hash = "18a7d6691c3e680b9b60a91d5401bf69ffaa028b1fd8a6aa9684149b6d7ab45b"
+content-hash = "d06eacfbb69be7f50a0ce901c7f6842e745855b5a88b9994977d9818e21f7d67"
diff --git a/pyproject.toml b/pyproject.toml
index b5a0dc86e8..f40f1fdd3b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -79,7 +79,7 @@ qiskit = ">=1.0.0"
 qiskit-aer = ">=0.14.0"
 sphinxcontrib-applehelp = "1.0.8"
 sphinx-gallery = "0.17.1"
-iqpopt = "2024.7.0"
+iqpopt = { git = "https://github.com/XanaduAI/iqpopt.git" }
 
 
 # Install a difference version of torch from PyPI as the one from PyTorch repo is not compatible with MacOS

From 8031665a2568d5037f99f224be20423946bcd89a Mon Sep 17 00:00:00 2001
From: Erik <erikrecio@gmail.com>
Date: Mon, 20 Jan 2025 19:32:21 +0100
Subject: [PATCH 7/7] small fix

---
 demonstrations/tutorial_iqpopt.py | 24 ++++++++++++++++++++----
 1 file changed, 20 insertions(+), 4 deletions(-)

diff --git a/demonstrations/tutorial_iqpopt.py b/demonstrations/tutorial_iqpopt.py
index 0c63b49347..2359241ddf 100644
--- a/demonstrations/tutorial_iqpopt.py
+++ b/demonstrations/tutorial_iqpopt.py
@@ -501,15 +501,27 @@ def penn_iqp_op_expval(params: np.ndarray, gates: list, op: np.ndarray, n_qubits
 sample = small_circuit.sample(params, shots=1)
 print(sample)
 
+# 
+
 probabilities = small_circuit.probs(params)
 print(probabilities)
 
-sample = large_circuit.sample(params, shots=1)
-print(sample)
+# 
 
-probabilities = large_circuit.probs(params)
-print(probabilities)
+try:
+    sample = large_circuit.sample(params, shots=1)
+    print(sample)
+except Exception as e:
+    print(e)
+
+# 
 
+try:
+    probabilities = large_circuit.probs(params)
+    print(probabilities)
+except Exception as e:
+    print(e)
+    
 ######################################################################
 # As we can see, we can't sample or know the probabilities of the circuit for the large one. The only
 
@@ -796,11 +808,15 @@ def loss_fn(params, circuit, ops, n_samples, key):
 plt.title("Histogram of the untrained circuit")
 plt.show()
 
+# 
+
 samples_trained = circuit.sample(trainer.final_params, 1000)
 plt.hist(np.sum(samples_trained, axis=1), bins=20, range=[0,20])
 plt.title("Histogram of the trained circuit")
 plt.show()
 
+# 
+
 plt.hist(np.sum(X_train, axis=1), bins=20, range=[0,20])
 plt.title("Histogram of the ground truth")
 plt.show()