/
models.py
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models.py
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"""The models for QNN classification on MNIST."""
import cirq
import sympy
import tensorflow as tf
import tensorflow_quantum as tfq
import utils
class CircuitLayerBuilder():
"""Building block for QNN."""
def __init__(self, data_qubits, readout):
"""__init__."""
self.data_qubits = data_qubits
self.readout = readout
def add_layer(self, circuit, gate, prefix):
"""Add layer."""
for i, qubit in enumerate(self.data_qubits):
symbol = sympy.Symbol(prefix + '-' + str(i))
circuit.append(gate(qubit, self.readout)**symbol)
def create_quantum_model(num_qubits=16, arch=['XX', 'ZZ']):
"""Create a QNN model circuit and readout operation to go along with it."""
h = utils.get_side(num_qubits)
data_qubits = cirq.GridQubit.rect(h, h) # a 4x4 grid.
readout = cirq.GridQubit(-1, -1) # a single qubit at [-1,-1]
circuit = cirq.Circuit()
# Prepare the readout qubit.
circuit.append(cirq.X(readout))
circuit.append(cirq.H(readout))
builder = CircuitLayerBuilder(
data_qubits=data_qubits,
readout=readout)
# Then add layers
for i, layer in enumerate(arch):
if layer == 'XX':
builder.add_layer(circuit, cirq.XX, "xx{}".format(i))
elif layer == 'ZZ':
builder.add_layer(circuit, cirq.ZZ, "zz{}".format(i))
else:
raise ValueError
# Finally, prepare the readout qubit.
circuit.append(cirq.H(readout))
return circuit, cirq.Z(readout)
def create_qnn(num_qubits=16, arch=['XX', 'ZZ']):
"""Create Keras model of QNN."""
model_circuit, model_readout = create_quantum_model(
num_qubits=16, arch=['XX', 'ZZ'])
model = tf.keras.Sequential([
tf.keras.layers.Input(shape=(), dtype=tf.string),
tfq.layers.PQC(model_circuit, model_readout)], name='qnn')
# For simplicity, we use Adam optimizer here. Parameter shift can be
# implemented by TensorFlow-Quantum, which converges much slower.
model.compile(
loss=tf.keras.losses.Hinge(),
optimizer=tf.keras.optimizers.Adam(),
metrics=[utils.hinge_accuracy])
return model
def create_quantum_model_feat(num_qubits=16, arch=['XX', 'ZZ']):
"""Create a QNN model circuit and readout operation to go along with it."""
h = utils.get_side(num_qubits)
data_qubits = cirq.GridQubit.rect(h, h) # a 4x4 grid.
readout = cirq.GridQubit(-1, -1) # a single qubit at [-1,-1]
circuit = cirq.Circuit()
# Prepare the readout qubit.
circuit.append(cirq.X(readout))
circuit.append(cirq.H(readout))
builder = CircuitLayerBuilder(
data_qubits=data_qubits,
readout=readout)
# Then add layers
for i, layer in enumerate(arch):
if layer == 'XX':
builder.add_layer(circuit, cirq.XX, "xx{}".format(i))
elif layer == 'ZZ':
builder.add_layer(circuit, cirq.ZZ, "zz{}".format(i))
else:
raise ValueError
# Finally, prepare the readout qubit.
circuit.append(cirq.H(readout))
return circuit, cirq.Z(readout), [cirq.Z(qubit) for qubit in data_qubits]
def create_qnn_feat(num_qubits=16, arch=['XX', 'ZZ']):
"""Create Keras model of QNN."""
model_circuit, model_readout, model_feature = create_quantum_model_feat(
num_qubits=16, arch=['XX', 'ZZ'])
model = tf.keras.Sequential([
tf.keras.layers.Input(shape=(), dtype=tf.string),
tfq.layers.PQC(model_circuit, model_feature)], name='qnn_feat')
return model
def create_dnn(num_qubits=16, model_name='dnn'):
"""A simple DNN model."""
h = utils.get_side(num_qubits)
model = tf.keras.Sequential(name=model_name)
model.add(tf.keras.layers.Flatten(input_shape=(h, h, 1)))
model.add(tf.keras.layers.Dense(2, activation='relu'))
model.add(tf.keras.layers.Dense(1))
return model
def create_cnn(num_qubits=16, model_name='cnn'):
"""Create a simple CNN model."""
h = utils.get_side(num_qubits)
model = tf.keras.Sequential(name=model_name)
model.add(tf.keras.layers.Reshape((1, h**2), input_shape=(h, h, 1)))
model.add(tf.keras.layers.Conv1D(16, 3, activation='relu', padding='same'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(1))
return model