estimator_from_aer.py qiskit >=0.46 compatible

README.md

@@ -20,52 +20,24 @@ Install from GitHub:
 ```bash
 pip install git+ssh://[email protected]/atlytle/dense-ev.git
 ```
-To run unit tests,
+A test function comparing results using abelian and dense grouping
+with exact results is available
 ```python
-from dense_ev._test_ops import run_unit_tests
+from dense_ev.test_estimator import test_random
 
-run_unit_tests()
+m = 3  # n_qubits
+run_unit_tests(m)
 ```
 
 ### Qiskit version compatibility
-Note that **dense_ev** specifies `qiskit < 0.43.0`, as the `Opflow` and
-`QuantumInstance` packages have been deprecated as of `Qiskit 0.43.0`.
-We plan to update the code to function with the new `primitives` as this 
-migration continues and more documentation becomes available.   
-**Update 2024-01-07**: Support for dense Pauli grouping 
+**2024-01-07**: Support for dense Pauli grouping 
 in the Aer `Estimator` primitive merged to `main` branch. 
-See usage example below.
+See usage example below.  
+**2024-05-30**: Functionality using `Opflow` and other deprecated classes
+has been removed. Package requirements changed from `qiskit < 0.43.0` 
+to `qiskit` (v1.0). Added `qiskit_aer` requirement.
 
 ## Usage
-Functionality for naive and QWC groupings is provided in Qiskit
-by the `PauliExpectation` class. `DensePauliExpectation` extends the functionality
-to dense optimal grouping, and may be used as a replacement for
-`PauliExpectation`:
-```python
-from dense_ev.dense_pauli_expectation import DensePauliExpectation
-
-# Simple expectation values:
-...
-
-ev_spec = StateFn(H).compose(psi)
-expectation = DensePauliExpectation().convert(ev_spec)
-
-...
-
-# VQE example
-...
-
-vqe = VQE(ansatz, optimizer=spsa, quantum_instance=qi,
-          callback=store_intermediate_result, 
-          expectation=DensePauliExpectation())
-
-result = vqe.compute_minimum_eigenvalue(operator=H)
-
-...
-
-
-```
-
 ### Estimator support
 The Aer implementation of `Estimator` is extended to incorporate
 dense Pauli grouping, and can be invoked using the keyword

src/dense_ev/estimator_from_aer.py

@@ -28,7 +28,7 @@
 import numpy as np
 from qiskit.circuit import ParameterExpression, QuantumCircuit
 from qiskit.compiler import transpile
-from qiskit.opflow import PauliSumOp
+#from qiskit.opflow import PauliSumOp
 from qiskit.primitives import BaseEstimator, EstimatorResult
 from qiskit.primitives.primitive_job import PrimitiveJob
 from qiskit.primitives.utils import _circuit_key, _observable_key, init_observable
@@ -104,9 +104,11 @@ def __init__(
                 If approximation is True, this parameter is ignored and assumed to be False.
         """
         super().__init__(options=run_options)
-        # if abelian_grouping == "DENSE":
-        #    m = 3
-        #    self.PO = PauliOrganizer(m)
+        # These three private attributes used to be created by super, but were deprecated in Qiskit
+        # 0.46. See https://github.com/Qiskit/qiskit/pull/11051
+        self._circuits = []
+        self._parameters = []
+        self._observables = []
         backend_options = {} if backend_options is None else backend_options
         method = (
             "density_matrix"
@@ -148,7 +150,7 @@ def _call(
     def _run(
         self,
         circuits: Sequence[QuantumCircuit],
-        observables: Sequence[BaseOperator | PauliSumOp],
+        observables: Sequence[BaseOperator],
         parameter_values: Sequence[Sequence[float]],
         **run_options,
     ) -> PrimitiveJob:
@@ -185,7 +187,8 @@ def _run(
             parameter_values,
             **run_options,
         )
-        job.submit()
+        # The public submit method was removed in Qiskit 0.46
+        (job.submit if hasattr(job, "submit") else job._submit)()  # pylint: disable=no-member
         return job
 
     def _compute(self, circuits, observables, parameter_values, run_options):
@@ -394,6 +397,8 @@ def _combine_circs(circuit: QuantumCircuit, meas_circuits: list[QuantumCircuit])
         circs = []
         for meas_circuit in meas_circuits:
             new_circ = circuit.copy()
+            #print(f"{new_circ.num_qubits = }")
+            #print(f"{meas_circuit.num_qubits = }")
             for creg in meas_circuit.cregs:
                 new_circ.add_register(creg)
             new_circ.compose(meas_circuit, inplace=True)

src/dense_ev/rmatrix.py

@@ -24,12 +24,12 @@
 from numpy.random import normal
 from scipy.stats import unitary_group
 
-from qiskit.opflow.primitive_ops import PauliOp
+#from qiskit.opflow.primitive_ops import PauliOp
 from qiskit.quantum_info import SparsePauliOp
 from qiskit.quantum_info.operators import Pauli
-from qiskit.opflow.list_ops.summed_op import SummedOp
-from qiskit.opflow.primitive_ops.pauli_sum_op import PauliSumOp
-from qiskit.opflow.converters import AbelianGrouper
+#from qiskit.opflow.list_ops.summed_op import SummedOp
+#from qiskit.opflow.primitive_ops.pauli_sum_op import PauliSumOp
+#from qiskit.opflow.converters import AbelianGrouper
 
 # from qiskit.opflow.converters.pauli_basis_change import PauliBasisChange
 # from qiskit.opflow.state_fns.state_fn import StateFn

src/dense_ev/test_estimator.py

@@ -42,14 +42,15 @@ def test_random(m):
     result_exact = estimator.run(state, H, shots=nshots).result().values
 
     # Abelian result.
-    nshots = 200000
-    approx = False
-    grouping = True
-    run_options = {"shots": nshots, "seed": seed}
-    estimator = Estimator(
-        run_options=run_options, abelian_grouping=grouping, approximation=approx
-    )
-    result_abelian = estimator.run(state, H, shots=nshots).result().values
+    # nshots = 200000
+    # approx = False
+    # grouping = True
+    # run_options = {"shots": nshots, "seed": seed}
+    # estimator = Estimator(
+    #     run_options=run_options, abelian_grouping=grouping,
+    #     approximation=approx
+    # )
+    #result_abelian = estimator.run(state, H, shots=nshots).result().values
 
     # Dense result.
     nshots = 200000
@@ -62,7 +63,7 @@ def test_random(m):
     result_dense = estimator.run(state, H, shots=nshots).result().values
 
     print(f"{result_exact = }")
-    print(f"{result_abelian = }")
+    #print(f"{result_abelian = }")
     print(f"{result_dense = }")