def circuit(params, wires, size=None, layers=None):
"""Define the quantum circuit as a stack of VQC layers
Args:
params (Array): Variational parameters in the form of NumPy array
wires (List[int]): List of qubit indices
size (int): Number of qubits
layers (List[int]): List of VQCs stacked up
"""
param_size = [models.param_shape(i, size) for i in layers]
param_idx_end = list(itertools.accumulate(param_size))
param_idx_start = [0] + param_idx_end[:-1]
for idx_start, idx_end, layer in zip(param_idx_start, param_idx_end, layers):
models.__dict__[f'circuit{layer:0>2d}'](params[idx_start:idx_end], wires)
def build_dummy_model(layers, size=2):
# Define the quantum device
device, wires = qml.device('default.qubit', wires=size), list(range(size))
param_size = [models.param_shape(i, size) for i in layers]
# Define the variational parameters
params = 2 * np.pi * (np.random.rand(sum(param_size)) - 0.5)
# Define the Hamiltonian operators
operators = [qml.PauliZ(i) @ qml.PauliZ(i + 1) for i in range(size - 1)] + \
[qml.PauliX(i) for i in range(size)]
coeffs = np.array([0.5
for i in range(size - 1)] + [1 for i in range(size)])
# Define the QNodeCollection
qnodes = qml.map(circuit, operators, device, measure="expval")
# Evaluate the QNodeCollection
def HNode(_params):
return np.dot(coeffs, qnodes(_params, size=size, layers=layers))
Op, OpG = HNode, qml.grad(HNode)
return Op, OpG, params, param_size
params (Array): Variational parameters in the form of NumPy array
wires (List[int]): List of qubit indices
size (int): Number of qubits
layers (List[int]): List of VQCs stacked up
"""
param_size = [models.param_shape(i, size) for i in layers]
param_idx_end = list(itertools.accumulate(param_size))
param_idx_start = [0] + param_idx_end[:-1]
for idx_start, idx_end, layer in zip(param_idx_start, param_idx_end, layers):
models.__dict__[f'circuit{layer:0>2d}'](params[idx_start:idx_end], wires)
# Define the quantum device
dev, wires = qml.device('default.qubit', wires=SIZE), list(range(SIZE))
param_sizes = [models.param_shape(i, SIZE) for i in LAYERS]
# Define the variational parameters
params = 2 * np.pi * (np.random.rand(sum(param_sizes)) - 0.5)
# Define the Hamiltonian operators
operators, coeffs = Hamiltonian()
# Define the QNodeCollection
qnodes = qml.map(circuit, operators, dev, measure="expval")
def HNode(params): # Evaluate the QNodeCollection
return np.dot(coeffs, qnodes(params, size=SIZE, layers=LAYERS))