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Quanvolutional Neural Networks
Quanvolutional Neural Networks
Published: March 23, 2020. Last updated: November 05, 2024.
In this demo we implement the Quanvolutional Neural Network, a quantum machine learning model originally introduced in Henderson et al. (2019).
Introduction
Classical convolution
The convolutional neural network (CNN) is a standard model in classical machine learning which is particularly suitable for processing images. The model is based on the idea of a convolution layer where, instead of processing the full input data with a global function, a local convolution is applied.
If the input is an image, small local regions are sequentially processed with the same kernel. The results obtained for each region are usually associated to different channels of a single output pixel. The union of all the output pixels produces a new image-like object, which can be further processed by additional layers.
Quantum convolution
One can extend the same idea also to the context of quantum variational circuits. A possible approach is given by the following procedure which is very similar to the one used in Ref. [1]. The scheme is also represented in the figure at the top of this tutorial.
A small region of the input image, in our example a \(2 \times 2\) square, is embedded into a quantum circuit. In this demo, this is achieved with parametrized rotations applied to the qubits initialized in the ground state.
A quantum computation, associated to a unitary \(U,\) is performed on the system. The unitary could be generated by a variational quantum circuit or, more simply, by a random circuit as proposed in Ref. [1].
The quantum system is finally measured, obtaining a list of classical expectation values. The measurement results could also be classically post-processed as proposed in Ref. [1] but, for simplicity, in this demo we directly use the raw expectation values.
Analogously to a classical convolution layer, each expectation value is mapped to a different channel of a single output pixel.
Iterating the same procedure over different regions, one can scan the full input image, producing an output object which will be structured as a multi-channel image.
The quantum convolution can be followed by further quantum layers or by classical layers.
The main difference with respect to a classical convolution is that a quantum circuit can generate highly complex kernels whose computation could be, at least in principle, classically intractable.
Note
In this tutorial we follow the approach of Ref. [1] in which a fixed non-trainable quantum circuit is used as a “quanvolution” kernel, while the subsequent classical layers are trained for the classification problem of interest. However, by leveraging the ability of PennyLane to evaluate gradients of quantum circuits, the quantum kernel could also be trained.
General setup
This Python code requires PennyLane with the TensorFlow interface and the plotting library matplotlib.
import pennylane as qml
from pennylane import numpy as np
from pennylane.templates import RandomLayers
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
Setting of the main hyper-parameters of the model
n_epochs = 30 # Number of optimization epochs
n_layers = 1 # Number of random layers
n_train = 50 # Size of the train dataset
n_test = 30 # Size of the test dataset
SAVE_PATH = "../_static/demonstration_assets/quanvolution/" # Data saving folder
PREPROCESS = True # If False, skip quantum processing and load data from SAVE_PATH
np.random.seed(0) # Seed for NumPy random number generator
tf.random.set_seed(0) # Seed for TensorFlow random number generator
Loading of the MNIST dataset
We import the MNIST dataset from Keras. To speedup the evaluation of this demo we use only a small number of training and test images. Obviously, better results are achievable when using the full dataset.
mnist_dataset = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist_dataset.load_data()
# Reduce dataset size
train_images = train_images[:n_train]
train_labels = train_labels[:n_train]
test_images = test_images[:n_test]
test_labels = test_labels[:n_test]
# Normalize pixel values within 0 and 1
train_images = train_images / 255
test_images = test_images / 255
# Add extra dimension for convolution channels
train_images = np.array(train_images[..., tf.newaxis], requires_grad=False)
test_images = np.array(test_images[..., tf.newaxis], requires_grad=False)
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
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Quantum circuit as a convolution kernel
We follow the scheme described in the introduction and represented in the figure at the top of this demo.
We initialize a PennyLane default.qubit device, simulating a system of \(4\) qubits.
The associated qnode represents the quantum circuit consisting of:
an embedding layer of local \(R_y\) rotations (with angles scaled by a factor of \(\pi\));
a random circuit of
n_layers;a final measurement in the computational basis, estimating \(4\) expectation values.
dev = qml.device("default.qubit", wires=4)
# Random circuit parameters
rand_params = np.random.uniform(high=2 * np.pi, size=(n_layers, 4))
@qml.qnode(dev)
def circuit(phi):
# Encoding of 4 classical input values
for j in range(4):
qml.RY(np.pi * phi[j], wires=j)
# Random quantum circuit
RandomLayers(rand_params, wires=list(range(4)))
# Measurement producing 4 classical output values
return [qml.expval(qml.PauliZ(j)) for j in range(4)]
The next function defines the convolution scheme:
the image is divided into squares of \(2 \times 2\) pixels;
each square is processed by the quantum circuit;
the \(4\) expectation values are mapped into \(4\) different channels of a single output pixel.
Note
This process halves the resolution of the input image. In the standard language of CNN, this would correspond to a convolution with a \(2 \times 2\) kernel and a stride equal to \(2.\)
def quanv(image):
"""Convolves the input image with many applications of the same quantum circuit."""
out = np.zeros((14, 14, 4))
# Loop over the coordinates of the top-left pixel of 2X2 squares
for j in range(0, 28, 2):
for k in range(0, 28, 2):
# Process a squared 2x2 region of the image with a quantum circuit
q_results = circuit(
[
image[j, k, 0],
image[j, k + 1, 0],
image[j + 1, k, 0],
image[j + 1, k + 1, 0]
]
)
# Assign expectation values to different channels of the output pixel (j/2, k/2)
for c in range(4):
out[j // 2, k // 2, c] = q_results[c]
return out
Quantum pre-processing of the dataset
Since we are not going to train the quantum convolution layer, it is more efficient to apply it as a “pre-processing” layer to all the images of our dataset. Later an entirely classical model will be directly trained and tested on the pre-processed dataset, avoiding unnecessary repetitions of quantum computations.
The pre-processed images will be saved in the folder SAVE_PATH.
Once saved, they can be directly loaded by setting PREPROCESS = False,
otherwise the quantum convolution is evaluated at each run of the code.
if PREPROCESS == True:
q_train_images = []
print("Quantum pre-processing of train images:")
for idx, img in enumerate(train_images):
print("{}/{} ".format(idx + 1, n_train), end="\r")
q_train_images.append(quanv(img))
q_train_images = np.asarray(q_train_images)
q_test_images = []
print("\nQuantum pre-processing of test images:")
for idx, img in enumerate(test_images):
print("{}/{} ".format(idx + 1, n_test), end="\r")
q_test_images.append(quanv(img))
q_test_images = np.asarray(q_test_images)
# Save pre-processed images
np.save(SAVE_PATH + "q_train_images.npy", q_train_images)
np.save(SAVE_PATH + "q_test_images.npy", q_test_images)
# Load pre-processed images
q_train_images = np.load(SAVE_PATH + "q_train_images.npy")
q_test_images = np.load(SAVE_PATH + "q_test_images.npy")
Quantum pre-processing of train images:
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Quantum pre-processing of test images:
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Let us visualize the effect of the quantum convolution layer on a batch of samples:
n_samples = 4
n_channels = 4
fig, axes = plt.subplots(1 + n_channels, n_samples, figsize=(10, 10))
for k in range(n_samples):
axes[0, 0].set_ylabel("Input")
if k != 0:
axes[0, k].yaxis.set_visible(False)
axes[0, k].imshow(train_images[k, :, :, 0], cmap="gray")
# Plot all output channels
for c in range(n_channels):
axes[c + 1, 0].set_ylabel("Output [ch. {}]".format(c))
if k != 0:
axes[c, k].yaxis.set_visible(False)
axes[c + 1, k].imshow(q_train_images[k, :, :, c], cmap="gray")
plt.tight_layout()
plt.show()
Below each input image, the \(4\) output channels generated by the quantum convolution are visualized in gray scale.
One can clearly notice the downsampling of the resolution and some local distortion introduced by the quantum kernel. On the other hand the global shape of the image is preserved, as expected for a convolution layer.
Hybrid quantum-classical model
After the application of the quantum convolution layer we feed the resulting features into a classical neural network that will be trained to classify the \(10\) different digits of the MNIST dataset.
We use a very simple model: just a fully connected layer with 10 output nodes with a final softmax activation function.
The model is compiled with a stochastic-gradient-descent optimizer, and a cross-entropy loss function.
def MyModel():
"""Initializes and returns a custom Keras model
which is ready to be trained."""
model = keras.models.Sequential([
keras.layers.Flatten(),
keras.layers.Dense(10, activation="softmax")
])
model.compile(
optimizer='adam',
loss="sparse_categorical_crossentropy",
metrics=["accuracy"],
)
return model
Training
We first initialize an instance of the model, then we train and validate it with the dataset that has been already pre-processed by a quantum convolution.
q_model = MyModel()
q_history = q_model.fit(
q_train_images,
train_labels,
validation_data=(q_test_images, test_labels),
batch_size=4,
epochs=n_epochs,
verbose=2,
)
Epoch 1/30
13/13 - 1s - 42ms/step - accuracy: 0.0800 - loss: 2.7543 - val_accuracy: 0.2667 - val_loss: 2.2957
Epoch 2/30
13/13 - 0s - 5ms/step - accuracy: 0.2600 - loss: 2.0134 - val_accuracy: 0.2667 - val_loss: 2.1067
Epoch 3/30
13/13 - 0s - 5ms/step - accuracy: 0.4800 - loss: 1.7121 - val_accuracy: 0.4000 - val_loss: 1.9390
Epoch 4/30
13/13 - 0s - 5ms/step - accuracy: 0.6600 - loss: 1.3875 - val_accuracy: 0.5333 - val_loss: 1.7809
Epoch 5/30
13/13 - 0s - 5ms/step - accuracy: 0.7800 - loss: 1.1472 - val_accuracy: 0.5667 - val_loss: 1.6658
Epoch 6/30
13/13 - 0s - 5ms/step - accuracy: 0.9000 - loss: 0.9561 - val_accuracy: 0.5333 - val_loss: 1.5772
Epoch 7/30
13/13 - 0s - 5ms/step - accuracy: 0.9200 - loss: 0.8004 - val_accuracy: 0.5667 - val_loss: 1.4992
Epoch 8/30
13/13 - 0s - 5ms/step - accuracy: 0.9400 - loss: 0.6773 - val_accuracy: 0.6000 - val_loss: 1.4364
Epoch 9/30
13/13 - 0s - 5ms/step - accuracy: 0.9800 - loss: 0.5776 - val_accuracy: 0.6000 - val_loss: 1.3854
Epoch 10/30
13/13 - 0s - 5ms/step - accuracy: 0.9800 - loss: 0.4971 - val_accuracy: 0.6000 - val_loss: 1.3426
Epoch 11/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.4318 - val_accuracy: 0.6333 - val_loss: 1.3065
Epoch 12/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.3779 - val_accuracy: 0.6333 - val_loss: 1.2759
Epoch 13/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.3334 - val_accuracy: 0.6333 - val_loss: 1.2499
Epoch 14/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.2962 - val_accuracy: 0.6333 - val_loss: 1.2275
Epoch 15/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.2649 - val_accuracy: 0.6333 - val_loss: 1.2080
Epoch 16/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.2383 - val_accuracy: 0.6333 - val_loss: 1.1910
Epoch 17/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.2157 - val_accuracy: 0.6333 - val_loss: 1.1760
Epoch 18/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1962 - val_accuracy: 0.6333 - val_loss: 1.1628
Epoch 19/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1793 - val_accuracy: 0.6333 - val_loss: 1.1509
Epoch 20/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1646 - val_accuracy: 0.6333 - val_loss: 1.1404
Epoch 21/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1517 - val_accuracy: 0.6333 - val_loss: 1.1308
Epoch 22/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1404 - val_accuracy: 0.6333 - val_loss: 1.1222
Epoch 23/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1303 - val_accuracy: 0.6667 - val_loss: 1.1143
Epoch 24/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1213 - val_accuracy: 0.6667 - val_loss: 1.1072
Epoch 25/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1133 - val_accuracy: 0.6667 - val_loss: 1.1006
Epoch 26/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1061 - val_accuracy: 0.6667 - val_loss: 1.0946
Epoch 27/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.0997 - val_accuracy: 0.6667 - val_loss: 1.0890
Epoch 28/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.0938 - val_accuracy: 0.6667 - val_loss: 1.0839
Epoch 29/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.0884 - val_accuracy: 0.6667 - val_loss: 1.0792
Epoch 30/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.0836 - val_accuracy: 0.6667 - val_loss: 1.0748
In order to compare the results achievable with and without the quantum convolution layer, we initialize also a “classical” instance of the model that will be directly trained and validated with the raw MNIST images (i.e., without quantum pre-processing).
c_model = MyModel()
c_history = c_model.fit(
train_images,
train_labels,
validation_data=(test_images, test_labels),
batch_size=4,
epochs=n_epochs,
verbose=2,
)
Epoch 1/30
13/13 - 0s - 38ms/step - accuracy: 0.2400 - loss: 2.2473 - val_accuracy: 0.1333 - val_loss: 2.2599
Epoch 2/30
13/13 - 0s - 5ms/step - accuracy: 0.6000 - loss: 1.8673 - val_accuracy: 0.2667 - val_loss: 2.0998
Epoch 3/30
13/13 - 0s - 5ms/step - accuracy: 0.6600 - loss: 1.5969 - val_accuracy: 0.4000 - val_loss: 1.9510
Epoch 4/30
13/13 - 0s - 5ms/step - accuracy: 0.7600 - loss: 1.3710 - val_accuracy: 0.4667 - val_loss: 1.8190
Epoch 5/30
13/13 - 0s - 5ms/step - accuracy: 0.8400 - loss: 1.1824 - val_accuracy: 0.5333 - val_loss: 1.7080
Epoch 6/30
13/13 - 0s - 5ms/step - accuracy: 0.8800 - loss: 1.0259 - val_accuracy: 0.5667 - val_loss: 1.6172
Epoch 7/30
13/13 - 0s - 5ms/step - accuracy: 0.8800 - loss: 0.8959 - val_accuracy: 0.5667 - val_loss: 1.5428
Epoch 8/30
13/13 - 0s - 5ms/step - accuracy: 0.9000 - loss: 0.7875 - val_accuracy: 0.6000 - val_loss: 1.4811
Epoch 9/30
13/13 - 0s - 5ms/step - accuracy: 0.9400 - loss: 0.6966 - val_accuracy: 0.6000 - val_loss: 1.4292
Epoch 10/30
13/13 - 0s - 5ms/step - accuracy: 0.9600 - loss: 0.6197 - val_accuracy: 0.6000 - val_loss: 1.3850
Epoch 11/30
13/13 - 0s - 5ms/step - accuracy: 0.9600 - loss: 0.5544 - val_accuracy: 0.6000 - val_loss: 1.3471
Epoch 12/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.4984 - val_accuracy: 0.5667 - val_loss: 1.3144
Epoch 13/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.4502 - val_accuracy: 0.6000 - val_loss: 1.2861
Epoch 14/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.4085 - val_accuracy: 0.6333 - val_loss: 1.2614
Epoch 15/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.3721 - val_accuracy: 0.6333 - val_loss: 1.2397
Epoch 16/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.3402 - val_accuracy: 0.7000 - val_loss: 1.2207
Epoch 17/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.3122 - val_accuracy: 0.7000 - val_loss: 1.2039
Epoch 18/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.2874 - val_accuracy: 0.7000 - val_loss: 1.1890
Epoch 19/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.2654 - val_accuracy: 0.7000 - val_loss: 1.1757
Epoch 20/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.2458 - val_accuracy: 0.7000 - val_loss: 1.1638
Epoch 21/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.2283 - val_accuracy: 0.7000 - val_loss: 1.1532
Epoch 22/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.2126 - val_accuracy: 0.7000 - val_loss: 1.1437
Epoch 23/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1985 - val_accuracy: 0.7000 - val_loss: 1.1350
Epoch 24/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1858 - val_accuracy: 0.7000 - val_loss: 1.1272
Epoch 25/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1742 - val_accuracy: 0.7000 - val_loss: 1.1201
Epoch 26/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1637 - val_accuracy: 0.7000 - val_loss: 1.1137
Epoch 27/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1542 - val_accuracy: 0.7000 - val_loss: 1.1078
Epoch 28/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1455 - val_accuracy: 0.6667 - val_loss: 1.1025
Epoch 29/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1375 - val_accuracy: 0.6667 - val_loss: 1.0976
Epoch 30/30
13/13 - 0s - 5ms/step - accuracy: 1.0000 - loss: 0.1302 - val_accuracy: 0.6667 - val_loss: 1.0931
Results
We can finally plot the test accuracy and the test loss with respect to the number of training epochs.
import matplotlib.pyplot as plt
plt.style.use("seaborn")
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 9))
ax1.plot(q_history.history["val_accuracy"], "-ob", label="With quantum layer")
ax1.plot(c_history.history["val_accuracy"], "-og", label="Without quantum layer")
ax1.set_ylabel("Accuracy")
ax1.set_ylim([0, 1])
ax1.set_xlabel("Epoch")
ax1.legend()
ax2.plot(q_history.history["val_loss"], "-ob", label="With quantum layer")
ax2.plot(c_history.history["val_loss"], "-og", label="Without quantum layer")
ax2.set_ylabel("Loss")
ax2.set_ylim(top=2.5)
ax2.set_xlabel("Epoch")
ax2.legend()
plt.tight_layout()
plt.show()
/home/runner/work/qml/qml/_build/demonstrations/tutorial_quanvolution.py:343: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.
plt.style.use("seaborn")
References
Maxwell Henderson, Samriddhi Shakya, Shashindra Pradhan, Tristan Cook. “Quanvolutional Neural Networks: Powering Image Recognition with Quantum Circuits.” arXiv:1904.04767, 2019.
About the author
Andrea Mari
Andrea obtained a PhD in quantum information theory from the University of Potsdam (Germany). He worked as a postdoc at Scuola Normale Superiore (Pisa, Italy) and as a remote researcher at Xanadu. Since 2020 is a Member of Technical Staff at Unitary ...
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