From 43d0f766e06f3d76e74bb76e80ee20608cf79417 Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Mon, 28 Nov 2022 17:30:14 +0400 Subject: [PATCH 01/31] example quantum anomaly detection --- examples/anomaly_detection/README.md | 1 + .../anomaly_detection/data/anomalous_data.npy | Bin 0 -> 2016640 bytes .../anomaly_detection/data/standard_data.npy | Bin 0 -> 1767296 bytes .../parameters/best_parameters.npy | Bin 0 -> 1064 bytes examples/anomaly_detection/results/ROC.png | Bin 0 -> 17999 bytes .../results/loss_distribution.png | Bin 0 -> 26098 bytes examples/anomaly_detection/test.py | 125 ++++++++++++++++++ examples/anomaly_detection/train.py | 91 +++++++++++++ 8 files changed, 217 insertions(+) create mode 100644 examples/anomaly_detection/README.md create mode 100644 examples/anomaly_detection/data/anomalous_data.npy create mode 100644 examples/anomaly_detection/data/standard_data.npy create mode 100644 examples/anomaly_detection/parameters/best_parameters.npy create mode 100644 examples/anomaly_detection/results/ROC.png create mode 100644 examples/anomaly_detection/results/loss_distribution.png create mode 100644 examples/anomaly_detection/test.py create mode 100644 examples/anomaly_detection/train.py diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md new file mode 100644 index 0000000000..30404ce4c5 --- /dev/null +++ b/examples/anomaly_detection/README.md @@ -0,0 +1 @@ +TODO \ No newline at end of file diff --git a/examples/anomaly_detection/data/anomalous_data.npy b/examples/anomaly_detection/data/anomalous_data.npy new file mode 100644 index 0000000000000000000000000000000000000000..0d57f3d40d15541b1b1ab496d5aeb479280a4466 GIT binary patch literal 2016640 zcmce930zLg|9^#4wl-x;B(h|QY<1>S_B|r|zGRK;vKFb(Y70q=k~VFWC?!Hvib^3u z)@+qz3;*Wc|8u|HYd%9gPrrG++|zyD^I6`rote+fIcLAg#4+QhN=vPm+N`s{e9ipT zItFcYgoXM#x@~k8TCQHZdhUwZma7+-)8B{AwOC_b`TLs1b61*I{(r~5{rdLRZPQ1; zlWv=>ZEBPhq-v0!o;U%Rn1SjMV%qr{iqG?{fVE0>1(v+(>ohocWB@ALz3n#?8*ywV 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+n_qubits=6 +n_layers=6 +q_compression=3 + +'''change to parameters/trained_params.npy for parameters trained with train.py''' +filename="parameters/best_parameters.npy" +n_params=(n_layers*n_qubits+q_compression)*3 + + +def make_encoder(n_qubits, n_layers, params, q_compression): + index=0 + encoder = Circuit(n_qubits) + for i in range(n_layers): + for j in range(n_qubits): + encoder.add(gates.RX(j, params[index])) + encoder.add(gates.RY(j, params[index+1])) + encoder.add(gates.RZ(j, params[index+2])) + index+=3 + + for j in range(n_qubits): + encoder.add(gates.CNOT(j,(j+1)%n_qubits)) + + for j in range(q_compression): + encoder.add(gates.RX(j, params[index])) + encoder.add(gates.RY(j, params[index+1])) + encoder.add(gates.RZ(j, params[index+2])) + index+=3 + return encoder + + +def compute_loss_test(encoder, vector): + reconstructed=encoder(vector) + #3q compression + loss=reconstructed.probabilities(qubits=[0])[0] + reconstructed.probabilities(qubits=[1])[0] + reconstructed.probabilities(qubits=[2])[0] + return loss + +'''Load data ''' +train_size=5000 +dataset_np_s=np.load("data/standard_data.npy") +dataset_np_s=dataset_np_s[train_size:] +dataset_s=tf.convert_to_tensor(dataset_np_s) +dataset_np_a=np.load("data/anomalous_data.npy") +dataset_np_a=dataset_np_a[train_size:] +dataset_a=tf.convert_to_tensor(dataset_np_a) + +params_np = np.load(filename) +trained_params=tf.convert_to_tensor(params_np) +encoder=make_encoder(n_qubits, n_layers, trained_params, q_compression) +encoder.compile() +#print("Circuit model summary") +#print(encoder.draw()) + +print("Computing losses...") +loss_s=[] +for i in range(len(dataset_np_s)): + loss_s.append(compute_loss_test(encoder,dataset_s[i]).numpy()) + +loss_a=[] +for i in range(len(dataset_np_a)): + loss_a.append(compute_loss_test(encoder,dataset_a[i]).numpy()) + +#np.save("results/losses_standard_data",loss_s) +#np.save("results/losses_anomalous_data",loss_a) + +'''Loss distribution graph ''' +plt.hist(loss_a,bins=60,histtype="step",color="red",label="Anomalous data") +plt.hist(loss_s,bins=60,histtype="step",color="blue",label="Standard data") +plt.ylabel('Number of images') +plt.xlabel('Loss value') +plt.title("Loss function distribution (MNIST dataset)") +plt.legend() +plt.savefig("results/loss_distribution.png") +plt.close() + +'''compute ROC curve''' +max1=np.amax(loss_s) +max2=np.amax(loss_a) +ma=max(max1,max2) +min1=np.amin(loss_s) +min2=np.amin(loss_a) +mi=min(min1,min2) + +tot_neg=len(loss_s) +tot_pos=len(loss_a) + +n_step=100. +n_step_int=100 +step=(ma-mi)/n_step +fpr=[] +tpr=[] +for i in range(n_step_int): + treshold=i*step+mi + c=0 + for j in range(tot_neg): + if loss_s[j]>treshold: + c+=1 + false_positive=c/float(tot_neg) + fpr.append(false_positive) + c=0 + for j in range(tot_pos): + if loss_a[j]>treshold: + c+=1 + true_positive=c/float(tot_pos) + tpr.append(true_positive) + + +plt.title("Receiver Operating Characteristic") +plt.plot(fpr, tpr) +plt.xlim([0, 1]) +plt.ylim([0, 1]) +plt.ylabel('True Positive Rate') +plt.xlabel('False Positive Rate') +plt.savefig("results/ROC.png") \ No newline at end of file diff --git a/examples/anomaly_detection/train.py b/examples/anomaly_detection/train.py new file mode 100644 index 0000000000..275369de32 --- /dev/null +++ b/examples/anomaly_detection/train.py @@ -0,0 +1,91 @@ +''' Training of the quantum autoencoder with 5000 standard samples (zero handwritten digits)''' + +import numpy as np +import tensorflow as tf +import qibo +import math +from qibo import gates +from qibo.models import Circuit +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.optimizers import schedules + +qibo.set_backend("tensorflow") + +dataset_np=np.load("data/standard_data.npy") +dataset=tf.convert_to_tensor(dataset_np) + +n_qubits=6 +n_layers=6 +q_compression=3 +batch_size=20 +nepochs = 20 +train_size=5000 +filename="parameters/trained_params" + +n_params=(n_layers*n_qubits+q_compression)*3 + + +def make_encoder(n_qubits, n_layers, params, q_compression): + index=0 + encoder = Circuit(n_qubits) + for i in range(n_layers): + for j in range(n_qubits): + encoder.add(gates.RX(j, params[index])) + encoder.add(gates.RY(j, params[index+1])) + encoder.add(gates.RZ(j, params[index+2])) + index+=3 + + for j in range(n_qubits): + encoder.add(gates.CNOT(j,(j+1)%n_qubits)) + + for j in range(q_compression): + encoder.add(gates.RX(j, params[index])) + encoder.add(gates.RY(j, params[index+1])) + encoder.add(gates.RZ(j, params[index+2])) + index+=3 + return encoder + +params = tf.Variable(tf.random.normal((n_params,))) +encoder=make_encoder(n_qubits, n_layers, params, q_compression) +print("Circuit model summary") +print(encoder.draw()) + +@tf.function +def compute_loss(encoder, params, vector): + encoder.set_parameters(params) + reconstructed=encoder(vector) + #3 qubits compression + loss=reconstructed.probabilities(qubits=[0])[0] + reconstructed.probabilities(qubits=[1])[0] + reconstructed.probabilities(qubits=[2])[0] + return loss + +@tf.function +def train_step(batch_size, encoder, params, dataset): + loss=0. + with tf.GradientTape() as tape: + for sample in range(batch_size): + loss=loss+compute_loss(encoder, params, dataset[sample]) + loss=loss/batch_size + grads = tape.gradient(loss, params) + optimizer.apply_gradients(zip([grads], [params])) + return loss + +steps_for_epoch=math.ceil(train_size/batch_size) +boundaries = [steps_for_epoch*3, steps_for_epoch*5, steps_for_epoch*8, steps_for_epoch*12, steps_for_epoch*15, steps_for_epoch*18] +values = [0.4, 0.2, 0.08, 0.04, 0.01, 0.005, 0.001] +learning_rate_fn = tf.keras.optimizers.schedules.PiecewiseConstantDecay( + boundaries, values) +optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate_fn) + +train=dataset[0:train_size] +trained_params=np.zeros((nepochs,n_params), dtype=float) + +print("Trained parameters will be saved in: ", filename) +print("Start training") +for epoch in range(nepochs): + tf.random.shuffle(train) + for i in range(steps_for_epoch): + loss=train_step(batch_size, encoder, params, train[i*batch_size: (i+1)*batch_size]) + trained_params[epoch]=params.numpy() + print("Epoch: %d Loss: %f" % (epoch+1,loss)) + +np.save(filename, trained_params[-1]) \ No newline at end of file From 14b86b4dc74741f654c213c54587d364f00133b9 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 28 Nov 2022 13:35:21 +0000 Subject: [PATCH 02/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- examples/anomaly_detection/README.md | 2 +- examples/anomaly_detection/test.py | 166 ++++++++++++++------------- examples/anomaly_detection/train.py | 97 +++++++++------- 3 files changed, 145 insertions(+), 120 deletions(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index 30404ce4c5..1333ed77b7 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -1 +1 @@ -TODO \ No newline at end of file +TODO diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index abb94ef069..5ee052f724 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -1,125 +1,131 @@ -'''Test the anomaly detection algorithm (loss function distribution and ROC curve) on 2000 standard (zeros) and 2000 anomalous (ones) samples''' +"""Test the anomaly detection algorithm (loss function distribution and ROC curve) on 2000 standard (zeros) and 2000 anomalous (ones) samples""" +import matplotlib.pyplot as plt import numpy as np import tensorflow as tf + import qibo from qibo import gates from qibo.models import Circuit -import matplotlib.pyplot as plt qibo.set_backend("tensorflow") -'''hyper-parameters must match the ones used for training''' -n_qubits=6 -n_layers=6 -q_compression=3 +"""hyper-parameters must match the ones used for training""" +n_qubits = 6 +n_layers = 6 +q_compression = 3 -'''change to parameters/trained_params.npy for parameters trained with train.py''' -filename="parameters/best_parameters.npy" -n_params=(n_layers*n_qubits+q_compression)*3 +"""change to parameters/trained_params.npy for parameters trained with train.py""" +filename = "parameters/best_parameters.npy" +n_params = (n_layers * n_qubits + q_compression) * 3 def make_encoder(n_qubits, n_layers, params, q_compression): - index=0 + index = 0 encoder = Circuit(n_qubits) for i in range(n_layers): for j in range(n_qubits): encoder.add(gates.RX(j, params[index])) - encoder.add(gates.RY(j, params[index+1])) - encoder.add(gates.RZ(j, params[index+2])) - index+=3 - + encoder.add(gates.RY(j, params[index + 1])) + encoder.add(gates.RZ(j, params[index + 2])) + index += 3 + for j in range(n_qubits): - encoder.add(gates.CNOT(j,(j+1)%n_qubits)) - + encoder.add(gates.CNOT(j, (j + 1) % n_qubits)) + for j in range(q_compression): encoder.add(gates.RX(j, params[index])) - encoder.add(gates.RY(j, params[index+1])) - encoder.add(gates.RZ(j, params[index+2])) - index+=3 + encoder.add(gates.RY(j, params[index + 1])) + encoder.add(gates.RZ(j, params[index + 2])) + index += 3 return encoder - - + + def compute_loss_test(encoder, vector): - reconstructed=encoder(vector) - #3q compression - loss=reconstructed.probabilities(qubits=[0])[0] + reconstructed.probabilities(qubits=[1])[0] + reconstructed.probabilities(qubits=[2])[0] + reconstructed = encoder(vector) + # 3q compression + loss = ( + reconstructed.probabilities(qubits=[0])[0] + + reconstructed.probabilities(qubits=[1])[0] + + reconstructed.probabilities(qubits=[2])[0] + ) return loss - -'''Load data ''' -train_size=5000 -dataset_np_s=np.load("data/standard_data.npy") -dataset_np_s=dataset_np_s[train_size:] -dataset_s=tf.convert_to_tensor(dataset_np_s) -dataset_np_a=np.load("data/anomalous_data.npy") -dataset_np_a=dataset_np_a[train_size:] -dataset_a=tf.convert_to_tensor(dataset_np_a) + + +"""Load data """ +train_size = 5000 +dataset_np_s = np.load("data/standard_data.npy") +dataset_np_s = dataset_np_s[train_size:] +dataset_s = tf.convert_to_tensor(dataset_np_s) +dataset_np_a = np.load("data/anomalous_data.npy") +dataset_np_a = dataset_np_a[train_size:] +dataset_a = tf.convert_to_tensor(dataset_np_a) params_np = np.load(filename) -trained_params=tf.convert_to_tensor(params_np) -encoder=make_encoder(n_qubits, n_layers, trained_params, q_compression) +trained_params = tf.convert_to_tensor(params_np) +encoder = make_encoder(n_qubits, n_layers, trained_params, q_compression) encoder.compile() -#print("Circuit model summary") -#print(encoder.draw()) +# print("Circuit model summary") +# print(encoder.draw()) print("Computing losses...") -loss_s=[] +loss_s = [] for i in range(len(dataset_np_s)): - loss_s.append(compute_loss_test(encoder,dataset_s[i]).numpy()) + loss_s.append(compute_loss_test(encoder, dataset_s[i]).numpy()) -loss_a=[] +loss_a = [] for i in range(len(dataset_np_a)): - loss_a.append(compute_loss_test(encoder,dataset_a[i]).numpy()) + loss_a.append(compute_loss_test(encoder, dataset_a[i]).numpy()) -#np.save("results/losses_standard_data",loss_s) -#np.save("results/losses_anomalous_data",loss_a) +# np.save("results/losses_standard_data",loss_s) +# np.save("results/losses_anomalous_data",loss_a) -'''Loss distribution graph ''' -plt.hist(loss_a,bins=60,histtype="step",color="red",label="Anomalous data") -plt.hist(loss_s,bins=60,histtype="step",color="blue",label="Standard data") -plt.ylabel('Number of images') -plt.xlabel('Loss value') +"""Loss distribution graph """ +plt.hist(loss_a, bins=60, histtype="step", color="red", label="Anomalous data") +plt.hist(loss_s, bins=60, histtype="step", color="blue", label="Standard data") +plt.ylabel("Number of images") +plt.xlabel("Loss value") plt.title("Loss function distribution (MNIST dataset)") plt.legend() plt.savefig("results/loss_distribution.png") plt.close() -'''compute ROC curve''' -max1=np.amax(loss_s) -max2=np.amax(loss_a) -ma=max(max1,max2) -min1=np.amin(loss_s) -min2=np.amin(loss_a) -mi=min(min1,min2) - -tot_neg=len(loss_s) -tot_pos=len(loss_a) - -n_step=100. -n_step_int=100 -step=(ma-mi)/n_step -fpr=[] -tpr=[] +"""compute ROC curve""" +max1 = np.amax(loss_s) +max2 = np.amax(loss_a) +ma = max(max1, max2) +min1 = np.amin(loss_s) +min2 = np.amin(loss_a) +mi = min(min1, min2) + +tot_neg = len(loss_s) +tot_pos = len(loss_a) + +n_step = 100.0 +n_step_int = 100 +step = (ma - mi) / n_step +fpr = [] +tpr = [] for i in range(n_step_int): - treshold=i*step+mi - c=0 - for j in range(tot_neg): - if loss_s[j]>treshold: - c+=1 - false_positive=c/float(tot_neg) - fpr.append(false_positive) - c=0 - for j in range(tot_pos): - if loss_a[j]>treshold: - c+=1 - true_positive=c/float(tot_pos) - tpr.append(true_positive) + treshold = i * step + mi + c = 0 + for j in range(tot_neg): + if loss_s[j] > treshold: + c += 1 + false_positive = c / float(tot_neg) + fpr.append(false_positive) + c = 0 + for j in range(tot_pos): + if loss_a[j] > treshold: + c += 1 + true_positive = c / float(tot_pos) + tpr.append(true_positive) plt.title("Receiver Operating Characteristic") plt.plot(fpr, tpr) plt.xlim([0, 1]) plt.ylim([0, 1]) -plt.ylabel('True Positive Rate') -plt.xlabel('False Positive Rate') -plt.savefig("results/ROC.png") \ No newline at end of file +plt.ylabel("True Positive Rate") +plt.xlabel("False Positive Rate") +plt.savefig("results/ROC.png") diff --git a/examples/anomaly_detection/train.py b/examples/anomaly_detection/train.py index 275369de32..6d7ed5809a 100644 --- a/examples/anomaly_detection/train.py +++ b/examples/anomaly_detection/train.py @@ -1,91 +1,110 @@ -''' Training of the quantum autoencoder with 5000 standard samples (zero handwritten digits)''' +""" Training of the quantum autoencoder with 5000 standard samples (zero handwritten digits)""" + +import math import numpy as np import tensorflow as tf +from tensorflow.keras.optimizers import Adam, schedules + import qibo -import math from qibo import gates from qibo.models import Circuit -from tensorflow.keras.optimizers import Adam -from tensorflow.keras.optimizers import schedules qibo.set_backend("tensorflow") -dataset_np=np.load("data/standard_data.npy") -dataset=tf.convert_to_tensor(dataset_np) +dataset_np = np.load("data/standard_data.npy") +dataset = tf.convert_to_tensor(dataset_np) -n_qubits=6 -n_layers=6 -q_compression=3 -batch_size=20 +n_qubits = 6 +n_layers = 6 +q_compression = 3 +batch_size = 20 nepochs = 20 -train_size=5000 -filename="parameters/trained_params" +train_size = 5000 +filename = "parameters/trained_params" -n_params=(n_layers*n_qubits+q_compression)*3 +n_params = (n_layers * n_qubits + q_compression) * 3 def make_encoder(n_qubits, n_layers, params, q_compression): - index=0 + index = 0 encoder = Circuit(n_qubits) for i in range(n_layers): for j in range(n_qubits): encoder.add(gates.RX(j, params[index])) - encoder.add(gates.RY(j, params[index+1])) - encoder.add(gates.RZ(j, params[index+2])) - index+=3 - + encoder.add(gates.RY(j, params[index + 1])) + encoder.add(gates.RZ(j, params[index + 2])) + index += 3 + for j in range(n_qubits): - encoder.add(gates.CNOT(j,(j+1)%n_qubits)) - + encoder.add(gates.CNOT(j, (j + 1) % n_qubits)) + for j in range(q_compression): encoder.add(gates.RX(j, params[index])) - encoder.add(gates.RY(j, params[index+1])) - encoder.add(gates.RZ(j, params[index+2])) - index+=3 + encoder.add(gates.RY(j, params[index + 1])) + encoder.add(gates.RZ(j, params[index + 2])) + index += 3 return encoder + params = tf.Variable(tf.random.normal((n_params,))) -encoder=make_encoder(n_qubits, n_layers, params, q_compression) +encoder = make_encoder(n_qubits, n_layers, params, q_compression) print("Circuit model summary") print(encoder.draw()) + @tf.function def compute_loss(encoder, params, vector): encoder.set_parameters(params) - reconstructed=encoder(vector) - #3 qubits compression - loss=reconstructed.probabilities(qubits=[0])[0] + reconstructed.probabilities(qubits=[1])[0] + reconstructed.probabilities(qubits=[2])[0] + reconstructed = encoder(vector) + # 3 qubits compression + loss = ( + reconstructed.probabilities(qubits=[0])[0] + + reconstructed.probabilities(qubits=[1])[0] + + reconstructed.probabilities(qubits=[2])[0] + ) return loss + @tf.function def train_step(batch_size, encoder, params, dataset): - loss=0. + loss = 0.0 with tf.GradientTape() as tape: for sample in range(batch_size): - loss=loss+compute_loss(encoder, params, dataset[sample]) - loss=loss/batch_size + loss = loss + compute_loss(encoder, params, dataset[sample]) + loss = loss / batch_size grads = tape.gradient(loss, params) optimizer.apply_gradients(zip([grads], [params])) return loss -steps_for_epoch=math.ceil(train_size/batch_size) -boundaries = [steps_for_epoch*3, steps_for_epoch*5, steps_for_epoch*8, steps_for_epoch*12, steps_for_epoch*15, steps_for_epoch*18] + +steps_for_epoch = math.ceil(train_size / batch_size) +boundaries = [ + steps_for_epoch * 3, + steps_for_epoch * 5, + steps_for_epoch * 8, + steps_for_epoch * 12, + steps_for_epoch * 15, + steps_for_epoch * 18, +] values = [0.4, 0.2, 0.08, 0.04, 0.01, 0.005, 0.001] learning_rate_fn = tf.keras.optimizers.schedules.PiecewiseConstantDecay( - boundaries, values) + boundaries, values +) optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate_fn) -train=dataset[0:train_size] -trained_params=np.zeros((nepochs,n_params), dtype=float) +train = dataset[0:train_size] +trained_params = np.zeros((nepochs, n_params), dtype=float) print("Trained parameters will be saved in: ", filename) print("Start training") for epoch in range(nepochs): tf.random.shuffle(train) for i in range(steps_for_epoch): - loss=train_step(batch_size, encoder, params, train[i*batch_size: (i+1)*batch_size]) - trained_params[epoch]=params.numpy() - print("Epoch: %d Loss: %f" % (epoch+1,loss)) + loss = train_step( + batch_size, encoder, params, train[i * batch_size : (i + 1) * batch_size] + ) + trained_params[epoch] = params.numpy() + print("Epoch: %d Loss: %f" % (epoch + 1, loss)) -np.save(filename, trained_params[-1]) \ No newline at end of file +np.save(filename, trained_params[-1]) From aa438b8e54ec4670f8d7da716d3a67c1a01f6534 Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Wed, 30 Nov 2022 17:01:00 +0400 Subject: [PATCH 03/31] fixes --- examples/anomaly_detection/README.md | 83 +++++- ...best_parameters.npy => trained_params.npy} | Bin examples/anomaly_detection/test.py | 245 +++++++++--------- examples/anomaly_detection/train.py | 218 +++++++++------- 4 files changed, 335 insertions(+), 211 deletions(-) rename examples/anomaly_detection/parameters/{best_parameters.npy => trained_params.npy} (100%) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index 1333ed77b7..d002990ede 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -1 +1,82 @@ -TODO +# Anomaly detection with parametrized quantum circuits + +Code at: [https://github.com/qiboteam/qibo/tree/master/examples/anomaly_detection](https://github.com/qiboteam/qibo/tree/master/examples/anomaly_detection). + +## Problem overview + +With the contemporary peak in interest regarding machine learning algorithms for their many applications in scientific research as well as industrial technology, we also have the simultaneous development of quantum computing. A combination of these two fields has lead to the development of quantum machine learning algorithms. +With this example we want to study the quantum version of a classic machine learning algorithm known as anomaly detection. This algorithm is implemented with an artificial neural network, in particular an autoencoder. In quantum machine learning, the autoencoder is realised using parametrized quantum circuits.The proposed algorithm is not meant to outperform the classical counterpart on classical data. This work aims to demonstrate that it is possible to use quantum variational algorithms for anomaly detection with possible future advantages in the analysis of quantum data. + +## How to run an example? + +The code is divided into two parts, training of the circuit (train.py) and performance evaluation (test.py). + +It is possible to define the following hyper-parameters for the training of the circuit (default have good performance): +- `n_layers` (int): number of ansatz circuit layers (default 6). +- `batch_size` (int): number of samples in one training batch (default 20). +- `nepochs` (int): number of training epochs (default 20). +- `train_size` (int): number of samples used for training, the remainings are used for performance evaluation, total samples are 7000 (default 5000). +- `filename` (str): location and file name where trained parameters are saved (default "parameters/trained_params"). +- `lr_boundaries` (list): epochs when learning rate is reduced (6 monotone growing values from 0 to nepochs) (default [3,6,9,12,15,18]). + +It is possible to define the following hyper-parameters for the performance evaluation of the circuit, these parameter must be equal to the ones used for training: +- `n_layers` (int): number of ansatz circuit layers (default 6). +- `train_size` (int): number of samples used for training, the remainings are used for performance evaluation, total samples are 7000 (default 5000). +- `filename` (str): location and file name of trained parameters to be tested (default "parameters/trained_params"). + +As an example, in order to use 4 layers in the variational quantum ansatz, you should execute the following command for training: + +```python +python train.py --n_layers 4 +``` +And the following command for performance evaluation: + +```python +python test.py --n_layers 4 --filename "parameters/trained_params" +``` + +## Background + +In this section we want to explain the main elements to understand the proposed quantum anomaly detection algorithm. + +### Anomaly detection + +Anomaly detection is a classification algorithm that allows to identify anomalous data. The advantage in using this machine learning technique is that only standard data are required for the training. +To achieve this it's necessary to train a particular artificial neural network (ANN) architecture called autoencoder. An autoencoder is composed of two main parts: encoder and decoder. + +![autoencoder](images/autoencoder.png) + +The encoder compresses initial data down to a small dimension (called latent dimension). The decoder inverts the process to reconstruct the original data from the compressed one. The parameters of the neural network are trained in order to minimise the difference between the initial and reconstructed data. The loss function (also called reconstruction loss) is therefore a measure of how accurately the reconstructed data resembles the original. + +For anomaly detection, the autoencoder is trained only on data samples belonging to the standard class. When the trained model is applied to new samples we expect the loss function to have different values for standard and anomalous data. +By choosing a threshold value for the loss function it is possible to classify an input based on whether its reconstruction loss lands above or below this threshold. The ROC curve (Receiver operating characteristic) indicates the true positive rate and false positive rate as a function of the threshold. This can help to set the threshold value in order to maximize true positive classifications and minimize false positives. + +### Parametrized quantum circuits + +A parametrized quantum circuit (PQC), also known as variational quantum circuits, can be used as the quantum counterpart of classical ANNs. In this kind of circuits the input information is stored in the initial state of the qubits. It can be stored as the phase (phase encoding) or in the states amplitudes (amplitude encoding). The initial state is transformed using rotation gates and entangling gates, usually controlled-not (C-NOT) gates. These gates can be organised in layers, in this circuit architecture one layer is composed of rotation gates (R_x, R_y, R_z) acting on all qubits followed by a series of C-NOT gates coupling neighbouring qubits. The trainable weights are the angles of rotation gates and can be trained using standard backpropagation (implemented with Tensorflow). + +![autoencoder](images/autoencoder.png) + +A quantum circuit implements a unitary, thus invertible, transformation on the initial state. This represents a great advantage for the autoencoder architecture, as the decoder can be taken as the inverse of the encoder quantum circuit. In order to compress information the encoder circuit has to disentangle and set to zero state a given number of qubits. The loss function is thus taken as the expected measurement values of these qubits. In this way, for the training of the circuit, it is necessary only the encoder. + +![autoencoder](images/autoencoder.png) + +## Algorithm implementation + +This section refers to the optimal algorithm parameters (default). + +Anomaly detection on handwritten digits is carried out on the MNIST dataset using zeros as the standard data and ones as the anomalous data. We compressed the images down to 8X8 pixels, in this way it is possible to encode initial data in six qubits. It is necessary to normalise the initial data array so that it can be encoded as state amplitudes. +The best configuration has been found with six layers and three compressed qubits. For entangling gates we have tested different C-NOT configurations, the one that gave better performance is reported in figure below. +Moreover this configuration requires only nearest neighbour connectivity for six qubits placed in a ring topology. In order to improve the performance, rotation gates with trainable parameters were added at the end of the encoder circuit for the three compressed qubits. A summary of the employed circuit is reported in the next figure. + +![autoencoder](images/autoencoder.png) + +### Training + +For the training of the circuit a dataset of 5000 images of zero handwritten digits has been employed. The loss function is the sum of the probabilities of the ground state for the first three qubits, thus these qubits are forced to the |1> state. +Training has been performed for 20 epochs using Adam optimizer, with a dynamic learning rate that spans from 0.4 in the first epochs to 0.001 in the last ones. This variable learning rate has helped reducing the problem of barren plateaus. + +### Performance evaluation + +To test the anomaly detection algorithm after the training phase, we have used 2000 standard images not used in the training and 2000 anomalous images. Figure below shows the loss distribution for the two test datasets. + diff --git a/examples/anomaly_detection/parameters/best_parameters.npy b/examples/anomaly_detection/parameters/trained_params.npy similarity index 100% rename from examples/anomaly_detection/parameters/best_parameters.npy rename to examples/anomaly_detection/parameters/trained_params.npy diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index 5ee052f724..fbeac2e204 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -1,131 +1,144 @@ -"""Test the anomaly detection algorithm (loss function distribution and ROC curve) on 2000 standard (zeros) and 2000 anomalous (ones) samples""" - +import argparse import matplotlib.pyplot as plt import numpy as np import tensorflow as tf - import qibo from qibo import gates from qibo.models import Circuit -qibo.set_backend("tensorflow") - -"""hyper-parameters must match the ones used for training""" -n_qubits = 6 -n_layers = 6 -q_compression = 3 +def main(n_layers, train_size, filename): + qibo.set_backend("tensorflow") -"""change to parameters/trained_params.npy for parameters trained with train.py""" -filename = "parameters/best_parameters.npy" -n_params = (n_layers * n_qubits + q_compression) * 3 + def make_encoder(n_qubits, n_layers, params, q_compression): + index = 0 + encoder = Circuit(n_qubits) + for i in range(n_layers): + for j in range(n_qubits): + encoder.add(gates.RX(j, params[index])) + encoder.add(gates.RY(j, params[index + 1])) + encoder.add(gates.RZ(j, params[index + 2])) + index += 3 + for j in range(n_qubits): + encoder.add(gates.CNOT(j, (j + 1) % n_qubits)) -def make_encoder(n_qubits, n_layers, params, q_compression): - index = 0 - encoder = Circuit(n_qubits) - for i in range(n_layers): - for j in range(n_qubits): + for j in range(q_compression): encoder.add(gates.RX(j, params[index])) encoder.add(gates.RY(j, params[index + 1])) encoder.add(gates.RZ(j, params[index + 2])) index += 3 - - for j in range(n_qubits): - encoder.add(gates.CNOT(j, (j + 1) % n_qubits)) - - for j in range(q_compression): - encoder.add(gates.RX(j, params[index])) - encoder.add(gates.RY(j, params[index + 1])) - encoder.add(gates.RZ(j, params[index + 2])) - index += 3 - return encoder - - -def compute_loss_test(encoder, vector): - reconstructed = encoder(vector) - # 3q compression - loss = ( - reconstructed.probabilities(qubits=[0])[0] - + reconstructed.probabilities(qubits=[1])[0] - + reconstructed.probabilities(qubits=[2])[0] + return encoder + + + def compute_loss_test(encoder, vector): + reconstructed = encoder(vector) + # 3q compression + loss = ( + reconstructed.probabilities(qubits=[0])[0] + + reconstructed.probabilities(qubits=[1])[0] + + reconstructed.probabilities(qubits=[2])[0] + ) + return loss + + + n_qubits = 6 + q_compression = 3 + dataset_np_s = np.load("data/standard_data.npy") + dataset_np_s = dataset_np_s[train_size:] + dataset_s = tf.convert_to_tensor(dataset_np_s) + dataset_np_a = np.load("data/anomalous_data.npy") + dataset_np_a = dataset_np_a[train_size:] + dataset_a = tf.convert_to_tensor(dataset_np_a) + + params_np = np.load(filename) + trained_params = tf.convert_to_tensor(params_np) + encoder = make_encoder(n_qubits, n_layers, trained_params, q_compression) + encoder.compile() + # print("Circuit model summary") + # print(encoder.draw()) + + print("Computing losses...") + loss_s = [] + for i in range(len(dataset_np_s)): + loss_s.append(compute_loss_test(encoder, dataset_s[i]).numpy()) + + loss_a = [] + for i in range(len(dataset_np_a)): + loss_a.append(compute_loss_test(encoder, dataset_a[i]).numpy()) + + # np.save("results/losses_standard_data",loss_s) + # np.save("results/losses_anomalous_data",loss_a) + + """Loss distribution graph """ + plt.hist(loss_a, bins=60, histtype="step", color="red", label="Anomalous data") + plt.hist(loss_s, bins=60, histtype="step", color="blue", label="Standard data") + plt.ylabel("Number of images") + plt.xlabel("Loss value") + plt.title("Loss function distribution (MNIST dataset)") + plt.legend() + plt.savefig("results/loss_distribution.png") + plt.close() + + """compute ROC curve""" + max1 = np.amax(loss_s) + max2 = np.amax(loss_a) + ma = max(max1, max2) + min1 = np.amin(loss_s) + min2 = np.amin(loss_a) + mi = min(min1, min2) + + tot_neg = len(loss_s) + tot_pos = len(loss_a) + + n_step = 100.0 + n_step_int = 100 + step = (ma - mi) / n_step + fpr = [] + tpr = [] + for i in range(n_step_int): + treshold = i * step + mi + c = 0 + for j in range(tot_neg): + if loss_s[j] > treshold: + c += 1 + false_positive = c / float(tot_neg) + fpr.append(false_positive) + c = 0 + for j in range(tot_pos): + if loss_a[j] > treshold: + c += 1 + true_positive = c / float(tot_pos) + tpr.append(true_positive) + + + plt.title("Receiver Operating Characteristic") + plt.plot(fpr, tpr) + plt.xlim([0, 1]) + plt.ylim([0, 1]) + plt.ylabel("True Positive Rate") + plt.xlabel("False Positive Rate") + plt.savefig("results/ROC.png") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--n_layers", + default=6, + type=int, + help="(int): number of ansatz circuit layers", + ) + parser.add_argument( + "--train_size", + default=5000, + type=int, + help="(int): number of samples used for training, the remainings are used for performance evaluation (total samples 7000)", + ) + parser.add_argument( + "--filename", + default="parameters/trained_params", + type=str, + help="(str): location and file name of trained parameters to be tested", ) - return loss - - -"""Load data """ -train_size = 5000 -dataset_np_s = np.load("data/standard_data.npy") -dataset_np_s = dataset_np_s[train_size:] -dataset_s = tf.convert_to_tensor(dataset_np_s) -dataset_np_a = np.load("data/anomalous_data.npy") -dataset_np_a = dataset_np_a[train_size:] -dataset_a = tf.convert_to_tensor(dataset_np_a) - -params_np = np.load(filename) -trained_params = tf.convert_to_tensor(params_np) -encoder = make_encoder(n_qubits, n_layers, trained_params, q_compression) -encoder.compile() -# print("Circuit model summary") -# print(encoder.draw()) - -print("Computing losses...") -loss_s = [] -for i in range(len(dataset_np_s)): - loss_s.append(compute_loss_test(encoder, dataset_s[i]).numpy()) - -loss_a = [] -for i in range(len(dataset_np_a)): - loss_a.append(compute_loss_test(encoder, dataset_a[i]).numpy()) - -# np.save("results/losses_standard_data",loss_s) -# np.save("results/losses_anomalous_data",loss_a) - -"""Loss distribution graph """ -plt.hist(loss_a, bins=60, histtype="step", color="red", label="Anomalous data") -plt.hist(loss_s, bins=60, histtype="step", color="blue", label="Standard data") -plt.ylabel("Number of images") -plt.xlabel("Loss value") -plt.title("Loss function distribution (MNIST dataset)") -plt.legend() -plt.savefig("results/loss_distribution.png") -plt.close() - -"""compute ROC curve""" -max1 = np.amax(loss_s) -max2 = np.amax(loss_a) -ma = max(max1, max2) -min1 = np.amin(loss_s) -min2 = np.amin(loss_a) -mi = min(min1, min2) - -tot_neg = len(loss_s) -tot_pos = len(loss_a) - -n_step = 100.0 -n_step_int = 100 -step = (ma - mi) / n_step -fpr = [] -tpr = [] -for i in range(n_step_int): - treshold = i * step + mi - c = 0 - for j in range(tot_neg): - if loss_s[j] > treshold: - c += 1 - false_positive = c / float(tot_neg) - fpr.append(false_positive) - c = 0 - for j in range(tot_pos): - if loss_a[j] > treshold: - c += 1 - true_positive = c / float(tot_pos) - tpr.append(true_positive) - - -plt.title("Receiver Operating Characteristic") -plt.plot(fpr, tpr) -plt.xlim([0, 1]) -plt.ylim([0, 1]) -plt.ylabel("True Positive Rate") -plt.xlabel("False Positive Rate") -plt.savefig("results/ROC.png") + args = parser.parse_args() + main(**vars(args)) \ No newline at end of file diff --git a/examples/anomaly_detection/train.py b/examples/anomaly_detection/train.py index 6d7ed5809a..fdcc7f2b0e 100644 --- a/examples/anomaly_detection/train.py +++ b/examples/anomaly_detection/train.py @@ -1,110 +1,140 @@ -""" Training of the quantum autoencoder with 5000 standard samples (zero handwritten digits)""" - +import argparse import math - import numpy as np import tensorflow as tf from tensorflow.keras.optimizers import Adam, schedules - import qibo from qibo import gates from qibo.models import Circuit -qibo.set_backend("tensorflow") - -dataset_np = np.load("data/standard_data.npy") -dataset = tf.convert_to_tensor(dataset_np) - -n_qubits = 6 -n_layers = 6 -q_compression = 3 -batch_size = 20 -nepochs = 20 -train_size = 5000 -filename = "parameters/trained_params" +def main(n_layers, batch_size, nepochs, train_size, filename, lr_boundaries): + qibo.set_backend("tensorflow") -n_params = (n_layers * n_qubits + q_compression) * 3 + def make_encoder(n_qubits, n_layers, params, q_compression): + index = 0 + encoder = Circuit(n_qubits) + for i in range(n_layers): + for j in range(n_qubits): + encoder.add(gates.RX(j, params[index])) + encoder.add(gates.RY(j, params[index + 1])) + encoder.add(gates.RZ(j, params[index + 2])) + index += 3 + for j in range(n_qubits): + encoder.add(gates.CNOT(j, (j + 1) % n_qubits)) -def make_encoder(n_qubits, n_layers, params, q_compression): - index = 0 - encoder = Circuit(n_qubits) - for i in range(n_layers): - for j in range(n_qubits): + for j in range(q_compression): encoder.add(gates.RX(j, params[index])) encoder.add(gates.RY(j, params[index + 1])) encoder.add(gates.RZ(j, params[index + 2])) index += 3 - - for j in range(n_qubits): - encoder.add(gates.CNOT(j, (j + 1) % n_qubits)) - - for j in range(q_compression): - encoder.add(gates.RX(j, params[index])) - encoder.add(gates.RY(j, params[index + 1])) - encoder.add(gates.RZ(j, params[index + 2])) - index += 3 - return encoder - - -params = tf.Variable(tf.random.normal((n_params,))) -encoder = make_encoder(n_qubits, n_layers, params, q_compression) -print("Circuit model summary") -print(encoder.draw()) - - -@tf.function -def compute_loss(encoder, params, vector): - encoder.set_parameters(params) - reconstructed = encoder(vector) - # 3 qubits compression - loss = ( - reconstructed.probabilities(qubits=[0])[0] - + reconstructed.probabilities(qubits=[1])[0] - + reconstructed.probabilities(qubits=[2])[0] - ) - return loss - - -@tf.function -def train_step(batch_size, encoder, params, dataset): - loss = 0.0 - with tf.GradientTape() as tape: - for sample in range(batch_size): - loss = loss + compute_loss(encoder, params, dataset[sample]) - loss = loss / batch_size - grads = tape.gradient(loss, params) - optimizer.apply_gradients(zip([grads], [params])) - return loss - - -steps_for_epoch = math.ceil(train_size / batch_size) -boundaries = [ - steps_for_epoch * 3, - steps_for_epoch * 5, - steps_for_epoch * 8, - steps_for_epoch * 12, - steps_for_epoch * 15, - steps_for_epoch * 18, -] -values = [0.4, 0.2, 0.08, 0.04, 0.01, 0.005, 0.001] -learning_rate_fn = tf.keras.optimizers.schedules.PiecewiseConstantDecay( - boundaries, values -) -optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate_fn) - -train = dataset[0:train_size] -trained_params = np.zeros((nepochs, n_params), dtype=float) - -print("Trained parameters will be saved in: ", filename) -print("Start training") -for epoch in range(nepochs): - tf.random.shuffle(train) - for i in range(steps_for_epoch): - loss = train_step( - batch_size, encoder, params, train[i * batch_size : (i + 1) * batch_size] + return encoder + + + @tf.function + def compute_loss(encoder, params, vector): + encoder.set_parameters(params) + reconstructed = encoder(vector) + # 3 qubits compression + loss = ( + reconstructed.probabilities(qubits=[0])[0] + + reconstructed.probabilities(qubits=[1])[0] + + reconstructed.probabilities(qubits=[2])[0] ) - trained_params[epoch] = params.numpy() - print("Epoch: %d Loss: %f" % (epoch + 1, loss)) - -np.save(filename, trained_params[-1]) + return loss + + + @tf.function + def train_step(batch_size, encoder, params, dataset): + loss = 0.0 + with tf.GradientTape() as tape: + for sample in range(batch_size): + loss = loss + compute_loss(encoder, params, dataset[sample]) + loss = loss / batch_size + grads = tape.gradient(loss, params) + optimizer.apply_gradients(zip([grads], [params])) + return loss + + + dataset_np = np.load("data/standard_data.npy") + dataset = tf.convert_to_tensor(dataset_np) + n_qubits = 6 + q_compression = 3 + n_params = (n_layers * n_qubits + q_compression) * 3 + params = tf.Variable(tf.random.normal((n_params,))) + encoder = make_encoder(n_qubits, n_layers, params, q_compression) + print("Circuit model summary") + print(encoder.draw()) + + steps_for_epoch = math.ceil(train_size / batch_size) + boundaries = [ + steps_for_epoch * lr_boundaries[0], + steps_for_epoch * lr_boundaries[1], + steps_for_epoch * lr_boundaries[2], + steps_for_epoch * lr_boundaries[3], + steps_for_epoch * lr_boundaries[4], + steps_for_epoch * lr_boundaries[5], + ] + values = [0.4, 0.2, 0.08, 0.04, 0.01, 0.005, 0.001] + learning_rate_fn = tf.keras.optimizers.schedules.PiecewiseConstantDecay( + boundaries, values + ) + optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate_fn) + + train = dataset[0:train_size] + trained_params = np.zeros((nepochs, n_params), dtype=float) + + print("Trained parameters will be saved in: ", filename) + print("Start training") + for epoch in range(nepochs): + tf.random.shuffle(train) + for i in range(steps_for_epoch): + loss = train_step( + batch_size, encoder, params, train[i * batch_size : (i + 1) * batch_size] + ) + trained_params[epoch] = params.numpy() + print("Epoch: %d Loss: %f" % (epoch + 1, loss)) + + np.save(filename, trained_params[-1]) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--n_layers", + default=6, + type=int, + help="(int): number of ansatz circuit layers", + ) + parser.add_argument( + "--batch_size", + default=20, + type=int, + help="(int): number of samples in one training batch", + ) + parser.add_argument( + "--nepochs", + default=20, + type=int, + help="(int): number of training epochs", + ) + parser.add_argument( + "--train_size", + default=5000, + type=int, + help="(int): number of samples used for training, the remainings are used for performance evaluation (total samples 7000)", + ) + parser.add_argument( + "--filename", + default="parameters/trained_params", + type=str, + help="(str): location and file name where trained parameters are saved", + ) + parser.add_argument( + "--lr_boundaries", + default=[3,6,9,12,15,18], + type=list, + help="(list): epochs when learning rate is reduced (6 monotone growing values from 0 to nepochs)", + ) + args = parser.parse_args() + main(**vars(args)) From 3e7c420ee5066422d709382d7fa27a9a0d2d6b90 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 30 Nov 2022 13:01:39 +0000 Subject: [PATCH 04/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- examples/anomaly_detection/README.md | 5 ++--- examples/anomaly_detection/test.py | 8 ++++---- examples/anomaly_detection/train.py | 13 ++++++++----- 3 files changed, 14 insertions(+), 12 deletions(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index d002990ede..7c6d307abc 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -42,7 +42,7 @@ In this section we want to explain the main elements to understand the proposed ### Anomaly detection Anomaly detection is a classification algorithm that allows to identify anomalous data. The advantage in using this machine learning technique is that only standard data are required for the training. -To achieve this it's necessary to train a particular artificial neural network (ANN) architecture called autoencoder. An autoencoder is composed of two main parts: encoder and decoder. +To achieve this it's necessary to train a particular artificial neural network (ANN) architecture called autoencoder. An autoencoder is composed of two main parts: encoder and decoder. ![autoencoder](images/autoencoder.png) @@ -74,9 +74,8 @@ Moreover this configuration requires only nearest neighbour connectivity for six ### Training For the training of the circuit a dataset of 5000 images of zero handwritten digits has been employed. The loss function is the sum of the probabilities of the ground state for the first three qubits, thus these qubits are forced to the |1> state. -Training has been performed for 20 epochs using Adam optimizer, with a dynamic learning rate that spans from 0.4 in the first epochs to 0.001 in the last ones. This variable learning rate has helped reducing the problem of barren plateaus. +Training has been performed for 20 epochs using Adam optimizer, with a dynamic learning rate that spans from 0.4 in the first epochs to 0.001 in the last ones. This variable learning rate has helped reducing the problem of barren plateaus. ### Performance evaluation To test the anomaly detection algorithm after the training phase, we have used 2000 standard images not used in the training and 2000 anomalous images. Figure below shows the loss distribution for the two test datasets. - diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index fbeac2e204..f5beff73cd 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -1,11 +1,14 @@ import argparse + import matplotlib.pyplot as plt import numpy as np import tensorflow as tf + import qibo from qibo import gates from qibo.models import Circuit + def main(n_layers, train_size, filename): qibo.set_backend("tensorflow") @@ -29,7 +32,6 @@ def make_encoder(n_qubits, n_layers, params, q_compression): index += 3 return encoder - def compute_loss_test(encoder, vector): reconstructed = encoder(vector) # 3q compression @@ -40,7 +42,6 @@ def compute_loss_test(encoder, vector): ) return loss - n_qubits = 6 q_compression = 3 dataset_np_s = np.load("data/standard_data.npy") @@ -110,7 +111,6 @@ def compute_loss_test(encoder, vector): true_positive = c / float(tot_pos) tpr.append(true_positive) - plt.title("Receiver Operating Characteristic") plt.plot(fpr, tpr) plt.xlim([0, 1]) @@ -141,4 +141,4 @@ def compute_loss_test(encoder, vector): help="(str): location and file name of trained parameters to be tested", ) args = parser.parse_args() - main(**vars(args)) \ No newline at end of file + main(**vars(args)) diff --git a/examples/anomaly_detection/train.py b/examples/anomaly_detection/train.py index fdcc7f2b0e..f194fc86d0 100644 --- a/examples/anomaly_detection/train.py +++ b/examples/anomaly_detection/train.py @@ -1,12 +1,15 @@ import argparse import math + import numpy as np import tensorflow as tf from tensorflow.keras.optimizers import Adam, schedules + import qibo from qibo import gates from qibo.models import Circuit + def main(n_layers, batch_size, nepochs, train_size, filename, lr_boundaries): qibo.set_backend("tensorflow") @@ -30,7 +33,6 @@ def make_encoder(n_qubits, n_layers, params, q_compression): index += 3 return encoder - @tf.function def compute_loss(encoder, params, vector): encoder.set_parameters(params) @@ -43,7 +45,6 @@ def compute_loss(encoder, params, vector): ) return loss - @tf.function def train_step(batch_size, encoder, params, dataset): loss = 0.0 @@ -55,7 +56,6 @@ def train_step(batch_size, encoder, params, dataset): optimizer.apply_gradients(zip([grads], [params])) return loss - dataset_np = np.load("data/standard_data.npy") dataset = tf.convert_to_tensor(dataset_np) n_qubits = 6 @@ -90,7 +90,10 @@ def train_step(batch_size, encoder, params, dataset): tf.random.shuffle(train) for i in range(steps_for_epoch): loss = train_step( - batch_size, encoder, params, train[i * batch_size : (i + 1) * batch_size] + batch_size, + encoder, + params, + train[i * batch_size : (i + 1) * batch_size], ) trained_params[epoch] = params.numpy() print("Epoch: %d Loss: %f" % (epoch + 1, loss)) @@ -132,7 +135,7 @@ def train_step(batch_size, encoder, params, dataset): ) parser.add_argument( "--lr_boundaries", - default=[3,6,9,12,15,18], + default=[3, 6, 9, 12, 15, 18], type=list, help="(list): epochs when learning rate is reduced (6 monotone growing values from 0 to nepochs)", ) From 1aa46ff210ad872edb16b36463d9d900d79087ec Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Mon, 5 Dec 2022 10:59:40 +0400 Subject: [PATCH 05/31] readme and tests --- examples/anomaly_detection/README.md | 20 +++++++++++------ examples/anomaly_detection/images/Fig1.png | Bin 0 -> 128828 bytes examples/anomaly_detection/images/Fig2.png | Bin 0 -> 49723 bytes examples/anomaly_detection/images/Fig3.png | Bin 0 -> 69242 bytes examples/anomaly_detection/images/Fig4.png | Bin 0 -> 46507 bytes examples/anomaly_detection/images/Fig5.png | Bin 0 -> 10956 bytes examples/anomaly_detection/images/Fig6.png | Bin 0 -> 11002 bytes examples/anomaly_detection/test.py | 2 +- examples/test_examples.py | 25 +++++++++++++++++++++ 9 files changed, 39 insertions(+), 8 deletions(-) create mode 100644 examples/anomaly_detection/images/Fig1.png create mode 100644 examples/anomaly_detection/images/Fig2.png create mode 100644 examples/anomaly_detection/images/Fig3.png create mode 100644 examples/anomaly_detection/images/Fig4.png create mode 100644 examples/anomaly_detection/images/Fig5.png create mode 100644 examples/anomaly_detection/images/Fig6.png diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index 7c6d307abc..7581e484be 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -19,10 +19,10 @@ It is possible to define the following hyper-parameters for the training of the - `filename` (str): location and file name where trained parameters are saved (default "parameters/trained_params"). - `lr_boundaries` (list): epochs when learning rate is reduced (6 monotone growing values from 0 to nepochs) (default [3,6,9,12,15,18]). -It is possible to define the following hyper-parameters for the performance evaluation of the circuit, these parameter must be equal to the ones used for training: +It is possible to define the following hyper-parameters for the performance evaluation of the circuit, n_layers must be equal to the one used for training: - `n_layers` (int): number of ansatz circuit layers (default 6). - `train_size` (int): number of samples used for training, the remainings are used for performance evaluation, total samples are 7000 (default 5000). -- `filename` (str): location and file name of trained parameters to be tested (default "parameters/trained_params"). +- `filename` (str): location and file name of trained parameters to be tested (default "parameters/trained_params.npy"). As an example, in order to use 4 layers in the variational quantum ansatz, you should execute the following command for training: @@ -32,7 +32,7 @@ python train.py --n_layers 4 And the following command for performance evaluation: ```python -python test.py --n_layers 4 --filename "parameters/trained_params" +python test.py --n_layers 4 ``` ## Background @@ -44,7 +44,7 @@ In this section we want to explain the main elements to understand the proposed Anomaly detection is a classification algorithm that allows to identify anomalous data. The advantage in using this machine learning technique is that only standard data are required for the training. To achieve this it's necessary to train a particular artificial neural network (ANN) architecture called autoencoder. An autoencoder is composed of two main parts: encoder and decoder. -![autoencoder](images/autoencoder.png) +![Autoencoder architecture](images/Fig1.png) The encoder compresses initial data down to a small dimension (called latent dimension). The decoder inverts the process to reconstruct the original data from the compressed one. The parameters of the neural network are trained in order to minimise the difference between the initial and reconstructed data. The loss function (also called reconstruction loss) is therefore a measure of how accurately the reconstructed data resembles the original. @@ -55,11 +55,11 @@ By choosing a threshold value for the loss function it is possible to classify a A parametrized quantum circuit (PQC), also known as variational quantum circuits, can be used as the quantum counterpart of classical ANNs. In this kind of circuits the input information is stored in the initial state of the qubits. It can be stored as the phase (phase encoding) or in the states amplitudes (amplitude encoding). The initial state is transformed using rotation gates and entangling gates, usually controlled-not (C-NOT) gates. These gates can be organised in layers, in this circuit architecture one layer is composed of rotation gates (R_x, R_y, R_z) acting on all qubits followed by a series of C-NOT gates coupling neighbouring qubits. The trainable weights are the angles of rotation gates and can be trained using standard backpropagation (implemented with Tensorflow). -![autoencoder](images/autoencoder.png) +![parametrized quantum circuit (one layer)](images/Fig2.png) A quantum circuit implements a unitary, thus invertible, transformation on the initial state. This represents a great advantage for the autoencoder architecture, as the decoder can be taken as the inverse of the encoder quantum circuit. In order to compress information the encoder circuit has to disentangle and set to zero state a given number of qubits. The loss function is thus taken as the expected measurement values of these qubits. In this way, for the training of the circuit, it is necessary only the encoder. -![autoencoder](images/autoencoder.png) +![Quantum autoencoder](images/Fig3.png) ## Algorithm implementation @@ -69,7 +69,7 @@ Anomaly detection on handwritten digits is carried out on the MNIST dataset usin The best configuration has been found with six layers and three compressed qubits. For entangling gates we have tested different C-NOT configurations, the one that gave better performance is reported in figure below. Moreover this configuration requires only nearest neighbour connectivity for six qubits placed in a ring topology. In order to improve the performance, rotation gates with trainable parameters were added at the end of the encoder circuit for the three compressed qubits. A summary of the employed circuit is reported in the next figure. -![autoencoder](images/autoencoder.png) +![Circuit ansatz](images/Fig4.png) ### Training @@ -79,3 +79,9 @@ Training has been performed for 20 epochs using Adam optimizer, with a dynamic l ### Performance evaluation To test the anomaly detection algorithm after the training phase, we have used 2000 standard images not used in the training and 2000 anomalous images. Figure below shows the loss distribution for the two test datasets. + +![Loss function distribution](images/Fig5.png) + +The ROC curve shows the rate of true positive with respect to the rate of false positive by moving the loss value threshold for the binary classification. + +![ROC curve](images/Fig6.png) \ No newline at end of file diff --git a/examples/anomaly_detection/images/Fig1.png b/examples/anomaly_detection/images/Fig1.png new file mode 100644 index 0000000000000000000000000000000000000000..7c072874d206dbddd100273b7911adf33291ad11 GIT binary patch literal 128828 zcmeFYbySqy+b@0t0ty1g3@B}&FqBA_#0W^&3|)$}bT<<6IHXAT(2aD1fP_**<4}Th zH`3>x=Nsoe-*>IEet-S`c@Jx`fVua+_H})(&lP*`n~y3=GGrvwBme-A$-a821^}dR z03edRP5^##+vle?_yf;TO-2$Z>8D)<|F~u*p(p_WWf7$3uL%Kw08mjtNP(aKzpwv? z!2ehTDxMwu1sZ?lq^6_};NcToBP1feeuIScCK)-!t=o6*LZFmX_wG~E(9+Q}Fg|$r z=rPlir_3xcRyKAH&SzZDxp{c`_yq)oghfPOh>1%`!e2^B%e<16lUGnwQdUt_Q%4{* zG_|yKboKNN42@pDF@9@eYG!U>Y4y(9#@5c>!O_Xt#nsK-!_&*#$Jg(@e?VZ+hv2_H 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zES)~%n1geR-J%-^GN0$7xFbz5kv6RwP=2II!G~*krlh67yK(}_Xi0-zIUzABz7A*~ zVX$Y^%25GFLAL!>gNRVReRrAPNTHtJ zY!qE`)WH7u0fE@x?}^FNcjX_uA2=!_?I7d<^kyDswEx|q`7itaUyYl<@b@ETt7}&p VJ#4s%Kp!dysb_+CbJ_9!{{hKlJ5c}t literal 0 HcmV?d00001 diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index f5beff73cd..8ee1e1c2ea 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -136,7 +136,7 @@ def compute_loss_test(encoder, vector): ) parser.add_argument( "--filename", - default="parameters/trained_params", + default="parameters/trained_params.npy", type=str, help="(str): location and file name of trained parameters to be tested", ) diff --git a/examples/test_examples.py b/examples/test_examples.py index 12e2de30b4..88f51069cf 100644 --- a/examples/test_examples.py +++ b/examples/test_examples.py @@ -318,3 +318,28 @@ def test_grover_example3(nqubits, num_1): sys.path[-1] = path os.chdir(path) run_script(args, script_name="example3.py") + + +@pytest.mark.parametrize("n_layers", [6]) +@pytest.mark.parametrize("batch_size", [20]) +@pytest.mark.parametrize("nepochs", [7]) +@pytest.mark.parametrize("train_size", [1000]) +@pytest.mark.parametrize("filename", ["parameters/test_params"]) +@pytest.mark.parametrize("lr_boundaries", [[1,2,3,4,5,6]]) +def test_anomalydetection_train(n_layers, batch_size, nepochs, train_size, filename): + args = locals() + path = os.path.join(base_dir, "anomaly_detection") + sys.path[-1] = path + os.chdir(path) + run_script(args, script_name="train.py") + + +@pytest.mark.parametrize("n_layers", [6]) +@pytest.mark.parametrize("train_size", [5000]) +@pytest.mark.parametrize("filename", ["parameters/trained_params"]) +def test_anomalydetection_test(n_layers, train_size, filename): + args = locals() + path = os.path.join(base_dir, "anomaly_detection") + sys.path[-1] = path + os.chdir(path) + run_script(args, script_name="test.py") \ No newline at end of file From 14a4f05b8f800be656d4c1a6c4b0eff5df966e11 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 5 Dec 2022 07:00:09 +0000 Subject: [PATCH 06/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- examples/anomaly_detection/README.md | 4 ++-- examples/test_examples.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index 7581e484be..ffa5227698 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -82,6 +82,6 @@ To test the anomaly detection algorithm after the training phase, we have used 2 ![Loss function distribution](images/Fig5.png) -The ROC curve shows the rate of true positive with respect to the rate of false positive by moving the loss value threshold for the binary classification. +The ROC curve shows the rate of true positive with respect to the rate of false positive by moving the loss value threshold for the binary classification. -![ROC curve](images/Fig6.png) \ No newline at end of file +![ROC curve](images/Fig6.png) diff --git a/examples/test_examples.py b/examples/test_examples.py index 88f51069cf..e085fea6ec 100644 --- a/examples/test_examples.py +++ b/examples/test_examples.py @@ -325,7 +325,7 @@ def test_grover_example3(nqubits, num_1): @pytest.mark.parametrize("nepochs", [7]) @pytest.mark.parametrize("train_size", [1000]) @pytest.mark.parametrize("filename", ["parameters/test_params"]) -@pytest.mark.parametrize("lr_boundaries", [[1,2,3,4,5,6]]) +@pytest.mark.parametrize("lr_boundaries", [[1, 2, 3, 4, 5, 6]]) def test_anomalydetection_train(n_layers, batch_size, nepochs, train_size, filename): args = locals() path = os.path.join(base_dir, "anomaly_detection") @@ -342,4 +342,4 @@ def test_anomalydetection_test(n_layers, train_size, filename): path = os.path.join(base_dir, "anomaly_detection") sys.path[-1] = path os.chdir(path) - run_script(args, script_name="test.py") \ No newline at end of file + run_script(args, script_name="test.py") From 8702a40fb8a92659227a000cbf9cb658131e317b Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Mon, 5 Dec 2022 11:11:04 +0400 Subject: [PATCH 07/31] fix tests --- examples/test_examples.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/test_examples.py b/examples/test_examples.py index e085fea6ec..e0567b4229 100644 --- a/examples/test_examples.py +++ b/examples/test_examples.py @@ -326,7 +326,7 @@ def test_grover_example3(nqubits, num_1): @pytest.mark.parametrize("train_size", [1000]) @pytest.mark.parametrize("filename", ["parameters/test_params"]) @pytest.mark.parametrize("lr_boundaries", [[1, 2, 3, 4, 5, 6]]) -def test_anomalydetection_train(n_layers, batch_size, nepochs, train_size, filename): +def test_anomalydetection_train(n_layers, batch_size, nepochs, train_size, filename, lr_boundaries): args = locals() path = os.path.join(base_dir, "anomaly_detection") sys.path[-1] = path From 1623cd159049b9e831350da039a529d3ef02dd07 Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Mon, 5 Dec 2022 13:43:39 +0400 Subject: [PATCH 08/31] precommit --- examples/anomaly_detection/test.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index 8ee1e1c2ea..5b6672a382 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -142,3 +142,5 @@ def compute_loss_test(encoder, vector): ) args = parser.parse_args() main(**vars(args)) + + #change From a7bd2570dc063943d48b31f724f8a4fbeeaef05b Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Mon, 5 Dec 2022 15:00:44 +0400 Subject: [PATCH 09/31] small fixes --- examples/anomaly_detection/README.md | 2 +- examples/anomaly_detection/results/ROC.png | Bin 17999 -> 0 bytes .../results/loss_distribution.png | Bin 26098 -> 0 bytes examples/anomaly_detection/test.py | 33 +++++++++++------- examples/anomaly_detection/train.py | 22 +++++++++--- examples/test_examples.py | 2 +- 6 files changed, 41 insertions(+), 18 deletions(-) delete mode 100644 examples/anomaly_detection/results/ROC.png delete mode 100644 examples/anomaly_detection/results/loss_distribution.png diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index ffa5227698..fe22344973 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -16,7 +16,7 @@ It is possible to define the following hyper-parameters for the training of the - `batch_size` (int): number of samples in one training batch (default 20). - `nepochs` (int): number of training epochs (default 20). - `train_size` (int): number of samples used for training, the remainings are used for performance evaluation, total samples are 7000 (default 5000). -- `filename` (str): location and file name where trained parameters are saved (default "parameters/trained_params"). +- `filename` (str): location and file name where trained parameters are saved (default "parameters/trained_params.npy"). - `lr_boundaries` (list): epochs when learning rate is reduced (6 monotone growing values from 0 to nepochs) (default [3,6,9,12,15,18]). 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qibo.set_backend("tensorflow") + #Circuit ansatz def make_encoder(n_qubits, n_layers, params, q_compression): index = 0 encoder = Circuit(n_qubits) @@ -32,9 +33,10 @@ def make_encoder(n_qubits, n_layers, params, q_compression): index += 3 return encoder + #Evaluate loss function (3 qubit compression) for one sample def compute_loss_test(encoder, vector): reconstructed = encoder(vector) - # 3q compression + # 3 qubits compression loss = ( reconstructed.probabilities(qubits=[0])[0] + reconstructed.probabilities(qubits=[1])[0] @@ -42,8 +44,11 @@ def compute_loss_test(encoder, vector): ) return loss + #Other hyperparameters n_qubits = 6 q_compression = 3 + + #Load and pre-process data dataset_np_s = np.load("data/standard_data.npy") dataset_np_s = dataset_np_s[train_size:] dataset_s = tf.convert_to_tensor(dataset_np_s) @@ -51,21 +56,26 @@ def compute_loss_test(encoder, vector): dataset_np_a = dataset_np_a[train_size:] dataset_a = tf.convert_to_tensor(dataset_np_a) - params_np = np.load(filename) - trained_params = tf.convert_to_tensor(params_np) - encoder = make_encoder(n_qubits, n_layers, trained_params, q_compression) - encoder.compile() - # print("Circuit model summary") - # print(encoder.draw()) + #Load trained parameters + trained_params_np = np.load(filename) + trained_params = tf.convert_to_tensor(trained_params_np) + + #Create and print encoder circuit + encoder_test = make_encoder(n_qubits, n_layers, trained_params, q_compression) + encoder_test.compile() + print("Circuit model summary") + print(encoder_test.draw()) print("Computing losses...") + #Compute loss for standard data loss_s = [] for i in range(len(dataset_np_s)): - loss_s.append(compute_loss_test(encoder, dataset_s[i]).numpy()) + loss_s.append(compute_loss_test(encoder_test, dataset_s[i]).numpy()) + #compute loss anomalous data loss_a = [] for i in range(len(dataset_np_a)): - loss_a.append(compute_loss_test(encoder, dataset_a[i]).numpy()) + loss_a.append(compute_loss_test(encoder_test, dataset_a[i]).numpy()) # np.save("results/losses_standard_data",loss_s) # np.save("results/losses_anomalous_data",loss_a) @@ -80,7 +90,7 @@ def compute_loss_test(encoder, vector): plt.savefig("results/loss_distribution.png") plt.close() - """compute ROC curve""" + """Compute ROC curve""" max1 = np.amax(loss_s) max2 = np.amax(loss_a) ma = max(max1, max2) @@ -111,6 +121,7 @@ def compute_loss_test(encoder, vector): true_positive = c / float(tot_pos) tpr.append(true_positive) + """Roc curve graph """ plt.title("Receiver Operating Characteristic") plt.plot(fpr, tpr) plt.xlim([0, 1]) @@ -142,5 +153,3 @@ def compute_loss_test(encoder, vector): ) args = parser.parse_args() main(**vars(args)) - - #change diff --git a/examples/anomaly_detection/train.py b/examples/anomaly_detection/train.py index f194fc86d0..34aeadd60a 100644 --- a/examples/anomaly_detection/train.py +++ b/examples/anomaly_detection/train.py @@ -13,6 +13,7 @@ def main(n_layers, batch_size, nepochs, train_size, filename, lr_boundaries): qibo.set_backend("tensorflow") + #Circuit ansatz def make_encoder(n_qubits, n_layers, params, q_compression): index = 0 encoder = Circuit(n_qubits) @@ -33,6 +34,7 @@ def make_encoder(n_qubits, n_layers, params, q_compression): index += 3 return encoder + #Evaluate loss function (3 qubit compression) for one sample @tf.function def compute_loss(encoder, params, vector): encoder.set_parameters(params) @@ -45,6 +47,7 @@ def compute_loss(encoder, params, vector): ) return loss + #One optimization step @tf.function def train_step(batch_size, encoder, params, dataset): loss = 0.0 @@ -56,16 +59,25 @@ def train_step(batch_size, encoder, params, dataset): optimizer.apply_gradients(zip([grads], [params])) return loss - dataset_np = np.load("data/standard_data.npy") - dataset = tf.convert_to_tensor(dataset_np) + #Other hyperparameters n_qubits = 6 q_compression = 3 + + #Load and pre-process data + dataset_np = np.load("data/standard_data.npy") + dataset = tf.convert_to_tensor(dataset_np) + train = dataset[0:train_size] + + #Initial random parameters n_params = (n_layers * n_qubits + q_compression) * 3 params = tf.Variable(tf.random.normal((n_params,))) + + #Create and print encoder circuit encoder = make_encoder(n_qubits, n_layers, params, q_compression) print("Circuit model summary") print(encoder.draw()) + #Define optimizer parameters steps_for_epoch = math.ceil(train_size / batch_size) boundaries = [ steps_for_epoch * lr_boundaries[0], @@ -81,9 +93,10 @@ def train_step(batch_size, encoder, params, dataset): ) optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate_fn) - train = dataset[0:train_size] + # This array contains the parameters at each epoch trained_params = np.zeros((nepochs, n_params), dtype=float) + #Training print("Trained parameters will be saved in: ", filename) print("Start training") for epoch in range(nepochs): @@ -98,6 +111,7 @@ def train_step(batch_size, encoder, params, dataset): trained_params[epoch] = params.numpy() print("Epoch: %d Loss: %f" % (epoch + 1, loss)) + #Save parameters of last epoch np.save(filename, trained_params[-1]) @@ -129,7 +143,7 @@ def train_step(batch_size, encoder, params, dataset): ) parser.add_argument( "--filename", - default="parameters/trained_params", + default="parameters/trained_params.npy", type=str, help="(str): location and file name where trained parameters are saved", ) diff --git a/examples/test_examples.py b/examples/test_examples.py index e0567b4229..14434fcd88 100644 --- a/examples/test_examples.py +++ b/examples/test_examples.py @@ -336,7 +336,7 @@ def test_anomalydetection_train(n_layers, batch_size, nepochs, train_size, filen @pytest.mark.parametrize("n_layers", [6]) @pytest.mark.parametrize("train_size", [5000]) -@pytest.mark.parametrize("filename", ["parameters/trained_params"]) +@pytest.mark.parametrize("filename", ["parameters/trained_params.npy"]) def test_anomalydetection_test(n_layers, train_size, filename): args = locals() path = os.path.join(base_dir, "anomaly_detection") From 8e7730da984b5a56fb8582ee0bf74910920610ac Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Mon, 5 Dec 2022 15:32:32 +0400 Subject: [PATCH 10/31] small fixes --- examples/README.md | 1 + examples/test_examples.py | 22 +++++++++++----------- 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/examples/README.md b/examples/README.md index 0a1a1db7fc..efc9eea474 100644 --- a/examples/README.md +++ b/examples/README.md @@ -21,6 +21,7 @@ physics problems. - [Maximal violation of Bell inequalities variationally](bell-variational/README.md) - [Feedback-based ALgorithm for Quantum OptimizatioN - FALQON](falqon/README.md) - [A general Grover model](grover/README.md) +- [Quantum anomaly detection](anomaly_detection/README.md) In the `benchmarks` folder we have included examples concerning: - A generic benchmark script for multiple circuits (`benchmarks/main.py`) diff --git a/examples/test_examples.py b/examples/test_examples.py index 14434fcd88..db09ddd113 100644 --- a/examples/test_examples.py +++ b/examples/test_examples.py @@ -321,25 +321,25 @@ def test_grover_example3(nqubits, num_1): @pytest.mark.parametrize("n_layers", [6]) -@pytest.mark.parametrize("batch_size", [20]) -@pytest.mark.parametrize("nepochs", [7]) -@pytest.mark.parametrize("train_size", [1000]) -@pytest.mark.parametrize("filename", ["parameters/test_params"]) -@pytest.mark.parametrize("lr_boundaries", [[1, 2, 3, 4, 5, 6]]) -def test_anomalydetection_train(n_layers, batch_size, nepochs, train_size, filename, lr_boundaries): +@pytest.mark.parametrize("train_size", [5000]) +@pytest.mark.parametrize("filename", ["parameters/trained_params.npy"]) +def test_anomalydetection_test(n_layers, train_size, filename): args = locals() path = os.path.join(base_dir, "anomaly_detection") sys.path[-1] = path os.chdir(path) - run_script(args, script_name="train.py") + run_script(args, script_name="test.py") @pytest.mark.parametrize("n_layers", [6]) -@pytest.mark.parametrize("train_size", [5000]) -@pytest.mark.parametrize("filename", ["parameters/trained_params.npy"]) -def test_anomalydetection_test(n_layers, train_size, filename): +@pytest.mark.parametrize("batch_size", [20]) +@pytest.mark.parametrize("nepochs", [7]) +@pytest.mark.parametrize("train_size", [1000]) +@pytest.mark.parametrize("filename", ["parameters/test_params"]) +@pytest.mark.parametrize("lr_boundaries", [[1, 2, 3, 4, 5, 6]]) +def test_anomalydetection_train(n_layers, batch_size, nepochs, train_size, filename, lr_boundaries): args = locals() path = os.path.join(base_dir, "anomaly_detection") sys.path[-1] = path os.chdir(path) - run_script(args, script_name="test.py") + run_script(args, script_name="train.py") From 5ad5a8e451ab61e054b03d74eb4af217d02399a9 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 5 Dec 2022 11:33:10 +0000 Subject: [PATCH 11/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- examples/anomaly_detection/test.py | 16 ++++++++-------- examples/anomaly_detection/train.py | 20 ++++++++++---------- examples/test_examples.py | 4 +++- 3 files changed, 21 insertions(+), 19 deletions(-) diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index 9f4b0d3b71..cb28d17699 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -12,7 +12,7 @@ def main(n_layers, train_size, filename): qibo.set_backend("tensorflow") - #Circuit ansatz + # Circuit ansatz def make_encoder(n_qubits, n_layers, params, q_compression): index = 0 encoder = Circuit(n_qubits) @@ -33,7 +33,7 @@ def make_encoder(n_qubits, n_layers, params, q_compression): index += 3 return encoder - #Evaluate loss function (3 qubit compression) for one sample + # Evaluate loss function (3 qubit compression) for one sample def compute_loss_test(encoder, vector): reconstructed = encoder(vector) # 3 qubits compression @@ -44,11 +44,11 @@ def compute_loss_test(encoder, vector): ) return loss - #Other hyperparameters + # Other hyperparameters n_qubits = 6 q_compression = 3 - #Load and pre-process data + # Load and pre-process data dataset_np_s = np.load("data/standard_data.npy") dataset_np_s = dataset_np_s[train_size:] dataset_s = tf.convert_to_tensor(dataset_np_s) @@ -56,23 +56,23 @@ def compute_loss_test(encoder, vector): dataset_np_a = dataset_np_a[train_size:] dataset_a = tf.convert_to_tensor(dataset_np_a) - #Load trained parameters + # Load trained parameters trained_params_np = np.load(filename) trained_params = tf.convert_to_tensor(trained_params_np) - #Create and print encoder circuit + # Create and print encoder circuit encoder_test = make_encoder(n_qubits, n_layers, trained_params, q_compression) encoder_test.compile() print("Circuit model summary") print(encoder_test.draw()) print("Computing losses...") - #Compute loss for standard data + # Compute loss for standard data loss_s = [] for i in range(len(dataset_np_s)): loss_s.append(compute_loss_test(encoder_test, dataset_s[i]).numpy()) - #compute loss anomalous data + # compute loss anomalous data loss_a = [] for i in range(len(dataset_np_a)): loss_a.append(compute_loss_test(encoder_test, dataset_a[i]).numpy()) diff --git a/examples/anomaly_detection/train.py b/examples/anomaly_detection/train.py index 34aeadd60a..fd47ef9f4b 100644 --- a/examples/anomaly_detection/train.py +++ b/examples/anomaly_detection/train.py @@ -13,7 +13,7 @@ def main(n_layers, batch_size, nepochs, train_size, filename, lr_boundaries): qibo.set_backend("tensorflow") - #Circuit ansatz + # Circuit ansatz def make_encoder(n_qubits, n_layers, params, q_compression): index = 0 encoder = Circuit(n_qubits) @@ -34,7 +34,7 @@ def make_encoder(n_qubits, n_layers, params, q_compression): index += 3 return encoder - #Evaluate loss function (3 qubit compression) for one sample + # Evaluate loss function (3 qubit compression) for one sample @tf.function def compute_loss(encoder, params, vector): encoder.set_parameters(params) @@ -47,7 +47,7 @@ def compute_loss(encoder, params, vector): ) return loss - #One optimization step + # One optimization step @tf.function def train_step(batch_size, encoder, params, dataset): loss = 0.0 @@ -59,25 +59,25 @@ def train_step(batch_size, encoder, params, dataset): optimizer.apply_gradients(zip([grads], [params])) return loss - #Other hyperparameters + # Other hyperparameters n_qubits = 6 q_compression = 3 - #Load and pre-process data + # Load and pre-process data dataset_np = np.load("data/standard_data.npy") dataset = tf.convert_to_tensor(dataset_np) train = dataset[0:train_size] - #Initial random parameters + # Initial random parameters n_params = (n_layers * n_qubits + q_compression) * 3 params = tf.Variable(tf.random.normal((n_params,))) - #Create and print encoder circuit + # Create and print encoder circuit encoder = make_encoder(n_qubits, n_layers, params, q_compression) print("Circuit model summary") print(encoder.draw()) - #Define optimizer parameters + # Define optimizer parameters steps_for_epoch = math.ceil(train_size / batch_size) boundaries = [ steps_for_epoch * lr_boundaries[0], @@ -96,7 +96,7 @@ def train_step(batch_size, encoder, params, dataset): # This array contains the parameters at each epoch trained_params = np.zeros((nepochs, n_params), dtype=float) - #Training + # Training print("Trained parameters will be saved in: ", filename) print("Start training") for epoch in range(nepochs): @@ -111,7 +111,7 @@ def train_step(batch_size, encoder, params, dataset): trained_params[epoch] = params.numpy() print("Epoch: %d Loss: %f" % (epoch + 1, loss)) - #Save parameters of last epoch + # Save parameters of last epoch np.save(filename, trained_params[-1]) diff --git a/examples/test_examples.py b/examples/test_examples.py index db09ddd113..1a67d5dad6 100644 --- a/examples/test_examples.py +++ b/examples/test_examples.py @@ -337,7 +337,9 @@ def test_anomalydetection_test(n_layers, train_size, filename): @pytest.mark.parametrize("train_size", [1000]) @pytest.mark.parametrize("filename", ["parameters/test_params"]) @pytest.mark.parametrize("lr_boundaries", [[1, 2, 3, 4, 5, 6]]) -def test_anomalydetection_train(n_layers, batch_size, nepochs, train_size, filename, lr_boundaries): +def test_anomalydetection_train( + n_layers, batch_size, nepochs, train_size, filename, lr_boundaries +): args = locals() path = os.path.join(base_dir, "anomaly_detection") sys.path[-1] = path From 52000f7ee291240df06c2b27df0c28c33d373a18 Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Mon, 5 Dec 2022 16:51:05 +0400 Subject: [PATCH 12/31] small fixes --- examples/anomaly_detection/README.md | 4 +- examples/anomaly_detection/test.py | 120 +++++++++++++++------------ examples/test_examples.py | 2 + 3 files changed, 72 insertions(+), 54 deletions(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index fe22344973..7da3e34739 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -17,12 +17,14 @@ It is possible to define the following hyper-parameters for the training of the - `nepochs` (int): number of training epochs (default 20). - `train_size` (int): number of samples used for training, the remainings are used for performance evaluation, total samples are 7000 (default 5000). - `filename` (str): location and file name where trained parameters are saved (default "parameters/trained_params.npy"). -- `lr_boundaries` (list): epochs when learning rate is reduced (6 monotone growing values from 0 to nepochs) (default [3,6,9,12,15,18]). +- `lr_boundaries` (list): epochs when learning rate is reduced, 6 monotone growing values from 0 to nepochs (default [3,6,9,12,15,18]). It is possible to define the following hyper-parameters for the performance evaluation of the circuit, n_layers must be equal to the one used for training: - `n_layers` (int): number of ansatz circuit layers (default 6). - `train_size` (int): number of samples used for training, the remainings are used for performance evaluation, total samples are 7000 (default 5000). - `filename` (str): location and file name of trained parameters to be tested (default "parameters/trained_params.npy"). +- `plot` (bool): make plots of ROC and loss function distribution (default True). +- `save_loss` (bool): save losses for standard and anomalous data (default False). As an example, in order to use 4 layers in the variational quantum ansatz, you should execute the following command for training: diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index cb28d17699..e78a098487 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -9,7 +9,7 @@ from qibo.models import Circuit -def main(n_layers, train_size, filename): +def main(n_layers, train_size, filename, plot, save_loss): qibo.set_backend("tensorflow") # Circuit ansatz @@ -77,58 +77,60 @@ def compute_loss_test(encoder, vector): for i in range(len(dataset_np_a)): loss_a.append(compute_loss_test(encoder_test, dataset_a[i]).numpy()) - # np.save("results/losses_standard_data",loss_s) - # np.save("results/losses_anomalous_data",loss_a) - - """Loss distribution graph """ - plt.hist(loss_a, bins=60, histtype="step", color="red", label="Anomalous data") - plt.hist(loss_s, bins=60, histtype="step", color="blue", label="Standard data") - plt.ylabel("Number of images") - plt.xlabel("Loss value") - plt.title("Loss function distribution (MNIST dataset)") - plt.legend() - plt.savefig("results/loss_distribution.png") - plt.close() - - """Compute ROC curve""" - max1 = np.amax(loss_s) - max2 = np.amax(loss_a) - ma = max(max1, max2) - min1 = np.amin(loss_s) - min2 = np.amin(loss_a) - mi = min(min1, min2) - - tot_neg = len(loss_s) - tot_pos = len(loss_a) - - n_step = 100.0 - n_step_int = 100 - step = (ma - mi) / n_step - fpr = [] - tpr = [] - for i in range(n_step_int): - treshold = i * step + mi - c = 0 - for j in range(tot_neg): - if loss_s[j] > treshold: - c += 1 - false_positive = c / float(tot_neg) - fpr.append(false_positive) - c = 0 - for j in range(tot_pos): - if loss_a[j] > treshold: - c += 1 - true_positive = c / float(tot_pos) - tpr.append(true_positive) - - """Roc curve graph """ - plt.title("Receiver Operating Characteristic") - plt.plot(fpr, tpr) - plt.xlim([0, 1]) - plt.ylim([0, 1]) - plt.ylabel("True Positive Rate") - plt.xlabel("False Positive Rate") - plt.savefig("results/ROC.png") + if save_loss: + np.save("results/losses_standard_data",loss_s) + np.save("results/losses_anomalous_data",loss_a) + + if plot==True: + """Loss distribution graph """ + plt.hist(loss_a, bins=60, histtype="step", color="red", label="Anomalous data") + plt.hist(loss_s, bins=60, histtype="step", color="blue", label="Standard data") + plt.ylabel("Number of images") + plt.xlabel("Loss value") + plt.title("Loss function distribution (MNIST dataset)") + plt.legend() + plt.savefig("results/loss_distribution.png") + plt.close() + + """Compute ROC curve""" + max1 = np.amax(loss_s) + max2 = np.amax(loss_a) + ma = max(max1, max2) + min1 = np.amin(loss_s) + min2 = np.amin(loss_a) + mi = min(min1, min2) + + tot_neg = len(loss_s) + tot_pos = len(loss_a) + + n_step = 100.0 + n_step_int = 100 + step = (ma - mi) / n_step + fpr = [] + tpr = [] + for i in range(n_step_int): + treshold = i * step + mi + c = 0 + for j in range(tot_neg): + if loss_s[j] > treshold: + c += 1 + false_positive = c / float(tot_neg) + fpr.append(false_positive) + c = 0 + for j in range(tot_pos): + if loss_a[j] > treshold: + c += 1 + true_positive = c / float(tot_pos) + tpr.append(true_positive) + + """Roc curve graph """ + plt.title("Receiver Operating Characteristic") + plt.plot(fpr, tpr) + plt.xlim([0, 1]) + plt.ylim([0, 1]) + plt.ylabel("True Positive Rate") + plt.xlabel("False Positive Rate") + plt.savefig("results/ROC.png") if __name__ == "__main__": @@ -151,5 +153,17 @@ def compute_loss_test(encoder, vector): type=str, help="(str): location and file name of trained parameters to be tested", ) + parser.add_argument( + "--plot", + default=True, + type=bool, + help="(bool): make plots of ROC and loss function distribution", + ) + parser.add_argument( + "--save_loss", + default=False, + type=bool, + help="(bool): save losses for standard and anomalous data", + ) args = parser.parse_args() main(**vars(args)) diff --git a/examples/test_examples.py b/examples/test_examples.py index 1a67d5dad6..0a030b6bdd 100644 --- a/examples/test_examples.py +++ b/examples/test_examples.py @@ -323,6 +323,8 @@ def test_grover_example3(nqubits, num_1): @pytest.mark.parametrize("n_layers", [6]) @pytest.mark.parametrize("train_size", [5000]) @pytest.mark.parametrize("filename", ["parameters/trained_params.npy"]) +@pytest.mark.parametrize("plot", [False]) +@pytest.mark.parametrize("save_loss", [False]) def test_anomalydetection_test(n_layers, train_size, filename): args = locals() path = os.path.join(base_dir, "anomaly_detection") From df55ccf9ea71d201965a78f1eac07a3223722f9b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 5 Dec 2022 12:52:23 +0000 Subject: [PATCH 13/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- examples/anomaly_detection/test.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index e78a098487..fadb22dd1d 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -78,11 +78,11 @@ def compute_loss_test(encoder, vector): loss_a.append(compute_loss_test(encoder_test, dataset_a[i]).numpy()) if save_loss: - np.save("results/losses_standard_data",loss_s) - np.save("results/losses_anomalous_data",loss_a) + np.save("results/losses_standard_data", loss_s) + np.save("results/losses_anomalous_data", loss_a) - if plot==True: - """Loss distribution graph """ + if plot == True: + """Loss distribution graph""" plt.hist(loss_a, bins=60, histtype="step", color="red", label="Anomalous data") plt.hist(loss_s, bins=60, histtype="step", color="blue", label="Standard data") plt.ylabel("Number of images") From 77243c0cedd257a1cc9bd6f48c4fe36abb5877e1 Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Mon, 5 Dec 2022 18:57:26 +0400 Subject: [PATCH 14/31] small fix --- examples/test_examples.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/test_examples.py b/examples/test_examples.py index 0a030b6bdd..f393d99c7a 100644 --- a/examples/test_examples.py +++ b/examples/test_examples.py @@ -325,7 +325,7 @@ def test_grover_example3(nqubits, num_1): @pytest.mark.parametrize("filename", ["parameters/trained_params.npy"]) @pytest.mark.parametrize("plot", [False]) @pytest.mark.parametrize("save_loss", [False]) -def test_anomalydetection_test(n_layers, train_size, filename): +def test_anomalydetection_test(n_layers, train_size, filename, plot, save_loss): args = locals() path = os.path.join(base_dir, "anomaly_detection") sys.path[-1] = path From 48c7315187e1456fcc6edfe92adc640b683dc570 Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Mon, 5 Dec 2022 19:45:07 +0400 Subject: [PATCH 15/31] small fix --- examples/anomaly_detection/test.py | 2 +- examples/test_examples.py | 15 +-------------- 2 files changed, 2 insertions(+), 15 deletions(-) diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index fadb22dd1d..da9d06c961 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -81,7 +81,7 @@ def compute_loss_test(encoder, vector): np.save("results/losses_standard_data", loss_s) np.save("results/losses_anomalous_data", loss_a) - if plot == True: + if plot: """Loss distribution graph""" plt.hist(loss_a, bins=60, histtype="step", color="red", label="Anomalous data") plt.hist(loss_s, bins=60, histtype="step", color="blue", label="Standard data") diff --git a/examples/test_examples.py b/examples/test_examples.py index f393d99c7a..fc59964a9b 100644 --- a/examples/test_examples.py +++ b/examples/test_examples.py @@ -320,23 +320,10 @@ def test_grover_example3(nqubits, num_1): run_script(args, script_name="example3.py") -@pytest.mark.parametrize("n_layers", [6]) -@pytest.mark.parametrize("train_size", [5000]) -@pytest.mark.parametrize("filename", ["parameters/trained_params.npy"]) -@pytest.mark.parametrize("plot", [False]) -@pytest.mark.parametrize("save_loss", [False]) -def test_anomalydetection_test(n_layers, train_size, filename, plot, save_loss): - args = locals() - path = os.path.join(base_dir, "anomaly_detection") - sys.path[-1] = path - os.chdir(path) - run_script(args, script_name="test.py") - - @pytest.mark.parametrize("n_layers", [6]) @pytest.mark.parametrize("batch_size", [20]) @pytest.mark.parametrize("nepochs", [7]) -@pytest.mark.parametrize("train_size", [1000]) +@pytest.mark.parametrize("train_size", [100]) @pytest.mark.parametrize("filename", ["parameters/test_params"]) @pytest.mark.parametrize("lr_boundaries", [[1, 2, 3, 4, 5, 6]]) def test_anomalydetection_train( From d79b02cf858375cc08c171da4364258ee4f5abdc Mon Sep 17 00:00:00 2001 From: Simone-Bordoni <101353773+Simone-Bordoni@users.noreply.github.com> Date: Fri, 9 Dec 2022 14:14:35 +0400 Subject: [PATCH 16/31] Delete 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z)g1X+5;EPC>-*{ouC;vZQEjb_fj>NUyEWIZE>aqInAGnB}#`$Lp<^={@Ili8Yb_wif9zS}oJzIuYE|L(55 zE6FQWOc>N;Ye4=5>`^UF& z7THL!f8uEAw0P-z)#X+ag~}7hZYhrNGXCwgR2$3}6aP2*G%~Gia+P?;w~A`H>pHDu zFU1-#Hr|P1tXXE!^_ntcO~C`1DMxMSsoE*_r?N4-3`blRI;wmZ-c!`C z^pW~Q8sMpBp1e^<&FckaiRYPHSg`2~M{M$edOr2hF@t=dqZfgy{JS+iP}FNq+=FGO z1`_E24}S%7;Nf2~N5%mn5Q6d@uf$^08~*(t!TFm*_sDV|8?%wka+YH$f#4xWoK7FB l;m2z9|35$CyWYGxph$99vO4pkNACpqQ&G^AFO Date: Mon, 12 Dec 2022 14:50:45 +0400 Subject: [PATCH 18/31] fixed --- examples/anomaly_detection/README.md | 61 ++++++++++++++-------------- examples/anomaly_detection/test.py | 35 +++++++++++++++- examples/anomaly_detection/train.py | 48 +++++++++++++++++++++- 3 files changed, 112 insertions(+), 32 deletions(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index 7da3e34739..2e483b3f35 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -7,36 +7,6 @@ Code at: [https://github.com/qiboteam/qibo/tree/master/examples/anomaly_detectio With the contemporary peak in interest regarding machine learning algorithms for their many applications in scientific research as well as industrial technology, we also have the simultaneous development of quantum computing. A combination of these two fields has lead to the development of quantum machine learning algorithms. With this example we want to study the quantum version of a classic machine learning algorithm known as anomaly detection. This algorithm is implemented with an artificial neural network, in particular an autoencoder. In quantum machine learning, the autoencoder is realised using parametrized quantum circuits.The proposed algorithm is not meant to outperform the classical counterpart on classical data. This work aims to demonstrate that it is possible to use quantum variational algorithms for anomaly detection with possible future advantages in the analysis of quantum data. -## How to run an example? - -The code is divided into two parts, training of the circuit (train.py) and performance evaluation (test.py). - -It is possible to define the following hyper-parameters for the training of the circuit (default have good performance): -- `n_layers` (int): number of ansatz circuit layers (default 6). -- `batch_size` (int): number of samples in one training batch (default 20). -- `nepochs` (int): number of training epochs (default 20). -- `train_size` (int): number of samples used for training, the remainings are used for performance evaluation, total samples are 7000 (default 5000). -- `filename` (str): location and file name where trained parameters are saved (default "parameters/trained_params.npy"). -- `lr_boundaries` (list): epochs when learning rate is reduced, 6 monotone growing values from 0 to nepochs (default [3,6,9,12,15,18]). - -It is possible to define the following hyper-parameters for the performance evaluation of the circuit, n_layers must be equal to the one used for training: -- `n_layers` (int): number of ansatz circuit layers (default 6). -- `train_size` (int): number of samples used for training, the remainings are used for performance evaluation, total samples are 7000 (default 5000). -- `filename` (str): location and file name of trained parameters to be tested (default "parameters/trained_params.npy"). -- `plot` (bool): make plots of ROC and loss function distribution (default True). -- `save_loss` (bool): save losses for standard and anomalous data (default False). - -As an example, in order to use 4 layers in the variational quantum ansatz, you should execute the following command for training: - -```python -python train.py --n_layers 4 -``` -And the following command for performance evaluation: - -```python -python test.py --n_layers 4 -``` - ## Background In this section we want to explain the main elements to understand the proposed quantum anomaly detection algorithm. @@ -87,3 +57,34 @@ To test the anomaly detection algorithm after the training phase, we have used 2 The ROC curve shows the rate of true positive with respect to the rate of false positive by moving the loss value threshold for the binary classification. ![ROC curve](images/Fig6.png) + +## How to run an example? + +The code is divided into two parts, training of the circuit (`train.py`) and performance evaluation (`test.py`). + +It is possible to define the following hyper-parameters for the training of the circuit (default have good performance): +- `n_layers` (int): number of ansatz circuit layers (default 6). +- `batch_size` (int): number of samples in one training batch (default 20). +- `nepochs` (int): number of training epochs (default 20). +- `train_size` (int): number of samples used for training, the remainings are used for performance evaluation, total samples are 7000 (default 5000). +- `filename` (str): location and file name where trained parameters are saved (default "parameters/trained_params.npy"). +- `lr_boundaries` (list): epochs when learning rate is reduced, 6 monotone growing values from 0 to nepochs (default [3,6,9,12,15,18]). + +It is possible to define the following hyper-parameters for the performance evaluation of the circuit, `n_layers` must be equal to the one used for training: +- `n_layers` (int): number of ansatz circuit layers (default 6). +- `train_size` (int): number of samples used for training, the remainings are used for performance evaluation, total samples are 7000 (default 5000). +- `filename` (str): location and file name of trained parameters to be tested (default "parameters/trained_params.npy"). +- `plot` (bool): make plots of ROC and loss function distribution (default True). +- `save_loss` (bool): save losses for standard and anomalous data (default False). + +As an example, in order to use 4 layers in the variational quantum ansatz, you should execute the following command for training: + +```python +python train.py --n_layers 4 +``` +And the following command for performance evaluation: + +```python +python test.py --n_layers 4 +``` + diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index da9d06c961..878c097409 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -10,10 +10,32 @@ def main(n_layers, train_size, filename, plot, save_loss): + """Implements performance evaluation of a trained circuit. + + Args: + n_layers (int): number of ansatz circuit layers (default 6). + train_size (int): number of samples used for training, the remainings are used for performance evaluation, total samples are 7000 (default 5000). + filename (str): location and file name of trained parameters to be tested (default "parameters/trained_params.npy"). + plot (bool): make plots of ROC and loss function distribution (default True). + save_loss (bool): save losses for standard and anomalous data (default False). + """ + qibo.set_backend("tensorflow") # Circuit ansatz def make_encoder(n_qubits, n_layers, params, q_compression): + """Create encoder quantum circuit. + + Args: + n_qubits (int): number of qubits in the circuit. + n_layers (int): number of ansatz circuit layers. + params (tf.Tensor): parameters of the circuit. + q_compression (int): number of compressed qubits. + + Returns: + encoder (qibo.models.Circuit): parametrized quantum circuit. + """ + index = 0 encoder = Circuit(n_qubits) for i in range(n_layers): @@ -35,6 +57,15 @@ def make_encoder(n_qubits, n_layers, params, q_compression): # Evaluate loss function (3 qubit compression) for one sample def compute_loss_test(encoder, vector): + """Evaluate loss function for one test sample. + + Args: + encoder (qibo.models.Circuit): parametrized quantum circuit (trained). + vector (tf.Tensor): test sample, in the form of 1d vector. + + Returns: + loss (tf.Variable): loss of the test sample. + """ reconstructed = encoder(vector) # 3 qubits compression loss = ( @@ -72,7 +103,7 @@ def compute_loss_test(encoder, vector): for i in range(len(dataset_np_s)): loss_s.append(compute_loss_test(encoder_test, dataset_s[i]).numpy()) - # compute loss anomalous data + # Compute loss for anomalous data loss_a = [] for i in range(len(dataset_np_a)): loss_a.append(compute_loss_test(encoder_test, dataset_a[i]).numpy()) @@ -81,7 +112,9 @@ def compute_loss_test(encoder, vector): np.save("results/losses_standard_data", loss_s) np.save("results/losses_anomalous_data", loss_a) + # Make graphs for performance analysis if plot: + """Loss distribution graph""" plt.hist(loss_a, bins=60, histtype="step", color="red", label="Anomalous data") plt.hist(loss_s, bins=60, histtype="step", color="blue", label="Standard data") diff --git a/examples/anomaly_detection/train.py b/examples/anomaly_detection/train.py index fd47ef9f4b..1e5297e578 100644 --- a/examples/anomaly_detection/train.py +++ b/examples/anomaly_detection/train.py @@ -11,10 +11,33 @@ def main(n_layers, batch_size, nepochs, train_size, filename, lr_boundaries): + """Implements training of quantum circuit. + + Args: + n_layers (int): number of ansatz circuit layers (default 6). + batch_size (int): number of samples in one training batch (default 20). + nepochs (int): number of training epochs (default 20). + train_size (int): number of samples used for training, the remainings are used for performance evaluation, total samples are 7000 (default 5000). + filename (str): location and file name where trained parameters are saved (default "parameters/trained_params.npy"). + lr_boundaries (list): epochs when learning rate is reduced, 6 monotone growing values from 0 to nepochs (default [3,6,9,12,15,18]). + """ + qibo.set_backend("tensorflow") # Circuit ansatz def make_encoder(n_qubits, n_layers, params, q_compression): + """Create encoder quantum circuit. + + Args: + n_qubits (int): number of qubits in the circuit. + n_layers (int): number of ansatz circuit layers. + params (tf.Variable): parameters of the circuit. + q_compression (int): number of compressed qubits. + + Returns: + encoder (qibo.models.Circuit): parametrized quantum circuit. + """ + index = 0 encoder = Circuit(n_qubits) for i in range(n_layers): @@ -37,6 +60,17 @@ def make_encoder(n_qubits, n_layers, params, q_compression): # Evaluate loss function (3 qubit compression) for one sample @tf.function def compute_loss(encoder, params, vector): + """Evaluate loss function for one train sample. + + Args: + encoder (qibo.models.Circuit): parametrized quantum circuit. + params (tf.Variable): parameters of the circuit. + vector (tf.Tensor): train sample, in the form of 1d vector. + + Returns: + loss (tf.Variable): loss of the training sample. + """ + encoder.set_parameters(params) reconstructed = encoder(vector) # 3 qubits compression @@ -50,6 +84,18 @@ def compute_loss(encoder, params, vector): # One optimization step @tf.function def train_step(batch_size, encoder, params, dataset): + """Evaluate loss function on one train batch. + + Args: + batch_size (int): number of samples in one training batch. + encoder (qibo.models.Circuit): parametrized quantum circuit. + params (tf.Variable): parameters of the circuit. + vector (tf.Tensor): train sample, in the form of 1d vector. + + Returns: + loss (tf.Variable): average loss of the training batch. + """ + loss = 0.0 with tf.GradientTape() as tape: for sample in range(batch_size): @@ -68,7 +114,7 @@ def train_step(batch_size, encoder, params, dataset): dataset = tf.convert_to_tensor(dataset_np) train = dataset[0:train_size] - # Initial random parameters + # Initialize random parameters n_params = (n_layers * n_qubits + q_compression) * 3 params = tf.Variable(tf.random.normal((n_params,))) From 68efa5fc2b61d6cac1d38c6c0e2bc5920b19e89d Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 12 Dec 2022 10:51:12 +0000 Subject: [PATCH 19/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- examples/anomaly_detection/README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index 2e483b3f35..13241b88e1 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -87,4 +87,3 @@ And the following command for performance evaluation: ```python python test.py --n_layers 4 ``` - From 0c39ce908ffc084b99b053068f1dfc471d6aa533 Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Mon, 12 Dec 2022 15:02:47 +0400 Subject: [PATCH 20/31] fixes --- examples/anomaly_detection/README.md | 16 ++++++++-------- examples/anomaly_detection/test.py | 4 ++-- examples/anomaly_detection/train.py | 8 ++++---- 3 files changed, 14 insertions(+), 14 deletions(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index 13241b88e1..0e96f750f6 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -1,11 +1,11 @@ -# Anomaly detection with parametrized quantum circuits +# Anomaly detection with variational quantum circuits Code at: [https://github.com/qiboteam/qibo/tree/master/examples/anomaly_detection](https://github.com/qiboteam/qibo/tree/master/examples/anomaly_detection). ## Problem overview With the contemporary peak in interest regarding machine learning algorithms for their many applications in scientific research as well as industrial technology, we also have the simultaneous development of quantum computing. A combination of these two fields has lead to the development of quantum machine learning algorithms. -With this example we want to study the quantum version of a classic machine learning algorithm known as anomaly detection. This algorithm is implemented with an artificial neural network, in particular an autoencoder. In quantum machine learning, the autoencoder is realised using parametrized quantum circuits.The proposed algorithm is not meant to outperform the classical counterpart on classical data. This work aims to demonstrate that it is possible to use quantum variational algorithms for anomaly detection with possible future advantages in the analysis of quantum data. +With this example we want to study the quantum version of a classic machine learning algorithm known as anomaly detection. This algorithm is implemented with an artificial neural network, in particular an autoencoder. In quantum machine learning, the autoencoder is realised using a variational quantum circuit. The proposed algorithm is not meant to outperform the classical counterpart on classical data. This work aims to demonstrate that it is possible to use quantum variational algorithms for anomaly detection with possible future advantages in the analysis of quantum data. ## Background @@ -13,21 +13,21 @@ In this section we want to explain the main elements to understand the proposed ### Anomaly detection -Anomaly detection is a classification algorithm that allows to identify anomalous data. The advantage in using this machine learning technique is that only standard data are required for the training. +Anomaly detection is a classification algorithm that allows to identify anomalous data. The advantage in using this machine learning technique is that only a dataset with non anomalous (standard) data samples is required for the training. To achieve this it's necessary to train a particular artificial neural network (ANN) architecture called autoencoder. An autoencoder is composed of two main parts: encoder and decoder. ![Autoencoder architecture](images/Fig1.png) -The encoder compresses initial data down to a small dimension (called latent dimension). The decoder inverts the process to reconstruct the original data from the compressed one. The parameters of the neural network are trained in order to minimise the difference between the initial and reconstructed data. The loss function (also called reconstruction loss) is therefore a measure of how accurately the reconstructed data resembles the original. +The encoder compresses initial data down to a small dimension (called latent dimension). The decoder inverts the process to reconstruct the original data from the compressed one. The parameters of the neural network are trained in order to minimize the difference between the initial and reconstructed data. The loss function (also called reconstruction loss) is therefore a measure of how accurately the reconstructed data resembles the original. For anomaly detection, the autoencoder is trained only on data samples belonging to the standard class. When the trained model is applied to new samples we expect the loss function to have different values for standard and anomalous data. -By choosing a threshold value for the loss function it is possible to classify an input based on whether its reconstruction loss lands above or below this threshold. The ROC curve (Receiver operating characteristic) indicates the true positive rate and false positive rate as a function of the threshold. This can help to set the threshold value in order to maximize true positive classifications and minimize false positives. +By choosing a threshold value for the loss function it is possible to classify an input based on whether its reconstruction loss lands above or below this threshold. The ROC curve (Receiver Operating Characteristic) indicates the true positive rate and false positive rate as a function of the threshold. This can help to set the threshold value in order to maximize true positive classifications and minimize false positives. -### Parametrized quantum circuits +### Variational quantum circuits -A parametrized quantum circuit (PQC), also known as variational quantum circuits, can be used as the quantum counterpart of classical ANNs. In this kind of circuits the input information is stored in the initial state of the qubits. It can be stored as the phase (phase encoding) or in the states amplitudes (amplitude encoding). The initial state is transformed using rotation gates and entangling gates, usually controlled-not (C-NOT) gates. These gates can be organised in layers, in this circuit architecture one layer is composed of rotation gates (R_x, R_y, R_z) acting on all qubits followed by a series of C-NOT gates coupling neighbouring qubits. The trainable weights are the angles of rotation gates and can be trained using standard backpropagation (implemented with Tensorflow). +A Variational Quantum Circuit (VQC), also known as parametrized quantum circuit, can be used as the quantum counterpart of classical ANNs. In this kind of circuits the input information is stored in the initial state of the qubits. It can be stored as the phase (phase encoding) or in the states amplitudes (amplitude encoding). The initial state is transformed using rotation gates and entangling gates, usually controlled-not (C-NOT) gates. These gates can be organised in layers, in this circuit architecture one layer is composed of rotation gates (R_x, R_y, R_z) acting on all qubits followed by a series of C-NOT gates coupling neighbouring qubits. The trainable weights are the angles of rotation gates and can be trained using standard backpropagation (implemented with Tensorflow). -![parametrized quantum circuit (one layer)](images/Fig2.png) +![variational quantum circuit (one layer)](images/Fig2.png) A quantum circuit implements a unitary, thus invertible, transformation on the initial state. This represents a great advantage for the autoencoder architecture, as the decoder can be taken as the inverse of the encoder quantum circuit. In order to compress information the encoder circuit has to disentangle and set to zero state a given number of qubits. The loss function is thus taken as the expected measurement values of these qubits. In this way, for the training of the circuit, it is necessary only the encoder. diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index 878c097409..964f5f1f10 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -33,7 +33,7 @@ def make_encoder(n_qubits, n_layers, params, q_compression): q_compression (int): number of compressed qubits. Returns: - encoder (qibo.models.Circuit): parametrized quantum circuit. + encoder (qibo.models.Circuit): variational quantum circuit. """ index = 0 @@ -60,7 +60,7 @@ def compute_loss_test(encoder, vector): """Evaluate loss function for one test sample. Args: - encoder (qibo.models.Circuit): parametrized quantum circuit (trained). + encoder (qibo.models.Circuit): variational quantum circuit (trained). vector (tf.Tensor): test sample, in the form of 1d vector. Returns: diff --git a/examples/anomaly_detection/train.py b/examples/anomaly_detection/train.py index 1e5297e578..cdc65bd2c5 100644 --- a/examples/anomaly_detection/train.py +++ b/examples/anomaly_detection/train.py @@ -11,7 +11,7 @@ def main(n_layers, batch_size, nepochs, train_size, filename, lr_boundaries): - """Implements training of quantum circuit. + """Implements training of variational quantum circuit. Args: n_layers (int): number of ansatz circuit layers (default 6). @@ -35,7 +35,7 @@ def make_encoder(n_qubits, n_layers, params, q_compression): q_compression (int): number of compressed qubits. Returns: - encoder (qibo.models.Circuit): parametrized quantum circuit. + encoder (qibo.models.Circuit): variational quantum circuit. """ index = 0 @@ -63,7 +63,7 @@ def compute_loss(encoder, params, vector): """Evaluate loss function for one train sample. Args: - encoder (qibo.models.Circuit): parametrized quantum circuit. + encoder (qibo.models.Circuit): variational quantum circuit. params (tf.Variable): parameters of the circuit. vector (tf.Tensor): train sample, in the form of 1d vector. @@ -88,7 +88,7 @@ def train_step(batch_size, encoder, params, dataset): Args: batch_size (int): number of samples in one training batch. - encoder (qibo.models.Circuit): parametrized quantum circuit. + encoder (qibo.models.Circuit): variational quantum circuit. params (tf.Variable): parameters of the circuit. vector (tf.Tensor): train sample, in the form of 1d vector. From 65c23eece1e9da7ece5debfd8d96c9ee482477e6 Mon Sep 17 00:00:00 2001 From: simone bordoni Date: Mon, 12 Dec 2022 15:08:47 +0400 Subject: [PATCH 21/31] last fixes --- examples/anomaly_detection/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index 0e96f750f6..e0bfd13883 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -46,7 +46,7 @@ Moreover this configuration requires only nearest neighbour connectivity for six ### Training For the training of the circuit a dataset of 5000 images of zero handwritten digits has been employed. The loss function is the sum of the probabilities of the ground state for the first three qubits, thus these qubits are forced to the |1> state. -Training has been performed for 20 epochs using Adam optimizer, with a dynamic learning rate that spans from 0.4 in the first epochs to 0.001 in the last ones. This variable learning rate has helped reducing the problem of barren plateaus. +Training has been performed for 20 epochs using [Adam optimizer](https://arxiv.org/pdf/1412.6980.pdf), with a dynamic learning rate that spans from 0.4 in the first epochs to 0.001 in the last ones. This variable learning rate has helped reducing the problem of [barren plateaus](https://arxiv.org/pdf/1803.11173.pdf). ### Performance evaluation From 3a4743a305e6a59a14e61e29449227d5d289b1b2 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 12 Dec 2022 11:10:45 +0000 Subject: [PATCH 22/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- examples/anomaly_detection/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index e0bfd13883..ff967f3ea6 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -23,7 +23,7 @@ The encoder compresses initial data down to a small dimension (called latent dim For anomaly detection, the autoencoder is trained only on data samples belonging to the standard class. When the trained model is applied to new samples we expect the loss function to have different values for standard and anomalous data. By choosing a threshold value for the loss function it is possible to classify an input based on whether its reconstruction loss lands above or below this threshold. The ROC curve (Receiver Operating Characteristic) indicates the true positive rate and false positive rate as a function of the threshold. This can help to set the threshold value in order to maximize true positive classifications and minimize false positives. -### Variational quantum circuits +### Variational quantum circuits A Variational Quantum Circuit (VQC), also known as parametrized quantum circuit, can be used as the quantum counterpart of classical ANNs. In this kind of circuits the input information is stored in the initial state of the qubits. It can be stored as the phase (phase encoding) or in the states amplitudes (amplitude encoding). The initial state is transformed using rotation gates and entangling gates, usually controlled-not (C-NOT) gates. These gates can be organised in layers, in this circuit architecture one layer is composed of rotation gates (R_x, R_y, R_z) acting on all qubits followed by a series of C-NOT gates coupling neighbouring qubits. The trainable weights are the angles of rotation gates and can be trained using standard backpropagation (implemented with Tensorflow). From c57ed2243650d361204db1c49c9493c28a4b616e Mon Sep 17 00:00:00 2001 From: Simone-Bordoni <101353773+Simone-Bordoni@users.noreply.github.com> Date: Wed, 14 Dec 2022 15:03:28 +0400 Subject: [PATCH 23/31] Update examples/anomaly_detection/README.md Co-authored-by: Matteo Robbiati <62071516+MatteoRobbiati@users.noreply.github.com> --- examples/anomaly_detection/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index ff967f3ea6..66e45a1153 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -5,7 +5,7 @@ Code at: [https://github.com/qiboteam/qibo/tree/master/examples/anomaly_detectio ## Problem overview With the contemporary peak in interest regarding machine learning algorithms for their many applications in scientific research as well as industrial technology, we also have the simultaneous development of quantum computing. A combination of these two fields has lead to the development of quantum machine learning algorithms. -With this example we want to study the quantum version of a classic machine learning algorithm known as anomaly detection. This algorithm is implemented with an artificial neural network, in particular an autoencoder. In quantum machine learning, the autoencoder is realised using a variational quantum circuit. The proposed algorithm is not meant to outperform the classical counterpart on classical data. This work aims to demonstrate that it is possible to use quantum variational algorithms for anomaly detection with possible future advantages in the analysis of quantum data. +With this example we want to study the quantum version of a classic machine learning algorithm known as [anomaly detection](https://arxiv.org/abs/2007.02500). This algorithm is implemented with an artificial neural network, in particular an autoencoder. In quantum machine learning, the autoencoder is realised using a variational quantum circuit. The proposed algorithm is not meant to outperform the classical counterpart on classical data. This work aims to demonstrate that it is possible to use quantum variational algorithms for anomaly detection with possible future advantages in the analysis of quantum data. ## Background From eb741a4d3a5f7ed85ae8ed01dfdcf423d78c2b36 Mon Sep 17 00:00:00 2001 From: Simone-Bordoni <101353773+Simone-Bordoni@users.noreply.github.com> Date: Wed, 14 Dec 2022 15:03:54 +0400 Subject: [PATCH 24/31] Update examples/anomaly_detection/README.md Co-authored-by: Matteo Robbiati <62071516+MatteoRobbiati@users.noreply.github.com> --- examples/anomaly_detection/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index 66e45a1153..13eb16da71 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -14,7 +14,7 @@ In this section we want to explain the main elements to understand the proposed ### Anomaly detection Anomaly detection is a classification algorithm that allows to identify anomalous data. The advantage in using this machine learning technique is that only a dataset with non anomalous (standard) data samples is required for the training. -To achieve this it's necessary to train a particular artificial neural network (ANN) architecture called autoencoder. An autoencoder is composed of two main parts: encoder and decoder. +To achieve this it's necessary to train a particular artificial neural network (ANN) architecture called [autoencoder](https://arxiv.org/abs/2003.05991). An autoencoder is composed of two main parts: encoder and decoder. ![Autoencoder architecture](images/Fig1.png) From 1d417d5a90349758456697bc9f4250ba6db66e59 Mon Sep 17 00:00:00 2001 From: Simone-Bordoni <101353773+Simone-Bordoni@users.noreply.github.com> Date: Wed, 14 Dec 2022 15:04:11 +0400 Subject: [PATCH 25/31] Update examples/anomaly_detection/README.md Co-authored-by: Matteo Robbiati <62071516+MatteoRobbiati@users.noreply.github.com> --- examples/anomaly_detection/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index 13eb16da71..006ba324b3 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -79,7 +79,7 @@ It is possible to define the following hyper-parameters for the performance eval As an example, in order to use 4 layers in the variational quantum ansatz, you should execute the following command for training: -```python +```bash python train.py --n_layers 4 ``` And the following command for performance evaluation: From 857b9cbb50359c5ee69d992633d410c62a76c05c Mon Sep 17 00:00:00 2001 From: Simone-Bordoni <101353773+Simone-Bordoni@users.noreply.github.com> Date: Wed, 14 Dec 2022 15:04:18 +0400 Subject: [PATCH 26/31] Update examples/anomaly_detection/README.md Co-authored-by: Matteo Robbiati <62071516+MatteoRobbiati@users.noreply.github.com> --- examples/anomaly_detection/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index 006ba324b3..ac045e4f15 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -84,6 +84,6 @@ python train.py --n_layers 4 ``` And the following command for performance evaluation: -```python +```bash python test.py --n_layers 4 ``` From 8320043214e9e857dcccc8eb2d766833292ea5a9 Mon Sep 17 00:00:00 2001 From: Simone-Bordoni <101353773+Simone-Bordoni@users.noreply.github.com> Date: Wed, 14 Dec 2022 15:04:40 +0400 Subject: [PATCH 27/31] Update examples/anomaly_detection/test.py Co-authored-by: Matteo Robbiati <62071516+MatteoRobbiati@users.noreply.github.com> --- examples/anomaly_detection/test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index 964f5f1f10..b88fdc82a1 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -33,7 +33,7 @@ def make_encoder(n_qubits, n_layers, params, q_compression): q_compression (int): number of compressed qubits. Returns: - encoder (qibo.models.Circuit): variational quantum circuit. + encoder (:class:`qibo.models.circuit.Circuit`): variational quantum circuit. """ index = 0 From 9074407d37b5e97ef5e259109a52a9e6bc4c1402 Mon Sep 17 00:00:00 2001 From: Simone-Bordoni <101353773+Simone-Bordoni@users.noreply.github.com> Date: Wed, 14 Dec 2022 15:04:59 +0400 Subject: [PATCH 28/31] Update examples/anomaly_detection/test.py Co-authored-by: Matteo Robbiati <62071516+MatteoRobbiati@users.noreply.github.com> --- examples/anomaly_detection/test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py index b88fdc82a1..6bf03a27b0 100644 --- a/examples/anomaly_detection/test.py +++ b/examples/anomaly_detection/test.py @@ -60,7 +60,7 @@ def compute_loss_test(encoder, vector): """Evaluate loss function for one test sample. Args: - encoder (qibo.models.Circuit): variational quantum circuit (trained). + encoder (:class:`qibo.models.circuit.Circuit`): variational quantum circuit (trained). vector (tf.Tensor): test sample, in the form of 1d vector. Returns: From 54753257dd0a9ae5f2d74fa112950cd0ddbcc060 Mon Sep 17 00:00:00 2001 From: Simone-Bordoni <101353773+Simone-Bordoni@users.noreply.github.com> Date: Wed, 14 Dec 2022 15:26:41 +0400 Subject: [PATCH 29/31] Apply suggestions from code review Co-authored-by: Matteo Robbiati <62071516+MatteoRobbiati@users.noreply.github.com> --- examples/anomaly_detection/train.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/anomaly_detection/train.py b/examples/anomaly_detection/train.py index cdc65bd2c5..a112160ed2 100644 --- a/examples/anomaly_detection/train.py +++ b/examples/anomaly_detection/train.py @@ -35,7 +35,7 @@ def make_encoder(n_qubits, n_layers, params, q_compression): q_compression (int): number of compressed qubits. Returns: - encoder (qibo.models.Circuit): variational quantum circuit. + encoder (:class:`qibo.models.circuit.Circuit`): variational quantum circuit. """ index = 0 @@ -63,7 +63,7 @@ def compute_loss(encoder, params, vector): """Evaluate loss function for one train sample. Args: - encoder (qibo.models.Circuit): variational quantum circuit. + encoder (:class:`qibo.models.circuit.Circuit`): variational quantum circuit. params (tf.Variable): parameters of the circuit. vector (tf.Tensor): train sample, in the form of 1d vector. @@ -88,7 +88,7 @@ def train_step(batch_size, encoder, params, dataset): Args: batch_size (int): number of samples in one training batch. - encoder (qibo.models.Circuit): variational quantum circuit. + encoder (:class:`qibo.models.circuit.Circuit`): variational quantum circuit. params (tf.Variable): parameters of the circuit. vector (tf.Tensor): train sample, in the form of 1d vector. From b9c99180c1d33bfa0e8ddd0b03e611552252cfde Mon Sep 17 00:00:00 2001 From: Matteo Robbiati <62071516+MatteoRobbiati@users.noreply.github.com> Date: Wed, 14 Dec 2022 15:37:54 +0100 Subject: [PATCH 30/31] Shifting autoencoder reference --- examples/anomaly_detection/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/anomaly_detection/README.md b/examples/anomaly_detection/README.md index ac045e4f15..3d14b099d3 100644 --- a/examples/anomaly_detection/README.md +++ b/examples/anomaly_detection/README.md @@ -5,7 +5,7 @@ Code at: [https://github.com/qiboteam/qibo/tree/master/examples/anomaly_detectio ## Problem overview With the contemporary peak in interest regarding machine learning algorithms for their many applications in scientific research as well as industrial technology, we also have the simultaneous development of quantum computing. A combination of these two fields has lead to the development of quantum machine learning algorithms. -With this example we want to study the quantum version of a classic machine learning algorithm known as [anomaly detection](https://arxiv.org/abs/2007.02500). This algorithm is implemented with an artificial neural network, in particular an autoencoder. In quantum machine learning, the autoencoder is realised using a variational quantum circuit. The proposed algorithm is not meant to outperform the classical counterpart on classical data. This work aims to demonstrate that it is possible to use quantum variational algorithms for anomaly detection with possible future advantages in the analysis of quantum data. +With this example we want to study the quantum version of a classic machine learning algorithm known as [anomaly detection](https://arxiv.org/abs/2007.02500). This algorithm is implemented with an artificial neural network, in particular an [autoencoder](https://arxiv.org/abs/2003.05991). In quantum machine learning, the autoencoder is realised using a variational quantum circuit. The proposed algorithm is not meant to outperform the classical counterpart on classical data. This work aims to demonstrate that it is possible to use quantum variational algorithms for anomaly detection with possible future advantages in the analysis of quantum data. ## Background @@ -14,7 +14,7 @@ In this section we want to explain the main elements to understand the proposed ### Anomaly detection Anomaly detection is a classification algorithm that allows to identify anomalous data. The advantage in using this machine learning technique is that only a dataset with non anomalous (standard) data samples is required for the training. -To achieve this it's necessary to train a particular artificial neural network (ANN) architecture called [autoencoder](https://arxiv.org/abs/2003.05991). An autoencoder is composed of two main parts: encoder and decoder. +To achieve this it's necessary to train a particular artificial neural network (ANN) architecture called autoencoder. An autoencoder is composed of two main parts: encoder and decoder. ![Autoencoder architecture](images/Fig1.png) From f57cf6144777c9bf901eb7ccd965320c3a1a42c7 Mon Sep 17 00:00:00 2001 From: Stefano Carrazza Date: Wed, 14 Dec 2022 17:00:26 +0100 Subject: [PATCH 31/31] linking documentation to sphinx --- doc/source/code-examples/applications.rst | 2 ++ doc/source/code-examples/tutorials/anomaly_detection/README.md | 1 + doc/source/code-examples/tutorials/anomaly_detection/images | 1 + 3 files changed, 4 insertions(+) create mode 120000 doc/source/code-examples/tutorials/anomaly_detection/README.md create mode 120000 doc/source/code-examples/tutorials/anomaly_detection/images diff --git a/doc/source/code-examples/applications.rst b/doc/source/code-examples/applications.rst index 3687a2b79f..aca52c059a 100644 --- a/doc/source/code-examples/applications.rst +++ b/doc/source/code-examples/applications.rst @@ -55,6 +55,7 @@ High-Energy Physics :maxdepth: 1 tutorials/qPDF/qPDF.ipynb + tutorials/anomaly_detection/README.md Quantum Physics @@ -105,6 +106,7 @@ Variational Quantum Circuits tutorials/unary/README.md tutorials/bell-variational/README.md tutorials/qPDF/qPDF.ipynb + tutorials/anomaly_detection/README.md Grover's Algorithm diff --git a/doc/source/code-examples/tutorials/anomaly_detection/README.md b/doc/source/code-examples/tutorials/anomaly_detection/README.md new file mode 120000 index 0000000000..631704e07d --- /dev/null +++ b/doc/source/code-examples/tutorials/anomaly_detection/README.md @@ -0,0 +1 @@ +../../../../../examples/anomaly_detection/README.md \ No newline at end of file diff --git a/doc/source/code-examples/tutorials/anomaly_detection/images b/doc/source/code-examples/tutorials/anomaly_detection/images new file mode 120000 index 0000000000..9bee242a0a --- /dev/null +++ b/doc/source/code-examples/tutorials/anomaly_detection/images @@ -0,0 +1 @@ +../../../../../examples/anomaly_detection/images/ \ No newline at end of file

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