qiskit_addon_qcbm.born_machine

Define the Quantum Circuit Born Machine.

Classes

QCBM	Quantum Circuit Born Machine
MMD	Maximum Mean Discrepancy

Module Contents

class QCBM(num_qubits, num_layers, ansatz=None, data=None)

Quantum Circuit Born Machine

num_layers

num_qubits

data = None

draw()

Draw the QCBM circuit.

print()

Provide the stats on the QCBM.

train(data=None, loss_fcn=None, num_iterations=10, num_shots=100, backend=FakeFez())

Train the QCBM.

compute_loss(params, ansatz_isa, sampler, loss_fcn, train_history, target_probs)

Return the value of the loss function.

Parameters:

• params (ndarray) – Array of ansatz parameters

• ansatz_isa (QuantumCircuit) – Parameterized transpiled ansatz

• sampler (SamplerV2) – Sampler primitive instance

• loss_fcn (python function) – Function using to compute the loss

• train_history (dict) – Dictionary for storing intermediate results

• target_probs (ndarray) – the actual probability distribution

Returns:

Scalar loss value

Return type:

float

plot_compare_model_and_target_probs(x_max, target_probs, qcbm_probs)

Compare the probabilities obtained with QCBM with the actual probability distribution.

class MMD(rbf_sigmas, nparange_hilbert_dim)

Maximum Mean Discrepancy

K

rbf_sigmas

k_expval(px, py)

mmd_loss(px, py)