def test_choi_to_super(): # Note: up to normalization, choi_to_super is equal to super_to_choi # and therefore we just run the following consistency test. for dim in (2, 4, 8, 16): rand_mat = np.random.rand(dim**2, dim**2) assert np.allclose(super_to_choi(choi_to_super(rand_mat)), rand_mat) assert np.allclose(choi_to_super(super_to_choi(rand_mat)), rand_mat)
def test_find_optimal_representation_depolarizing_two_qubit_gates(circ_type): """Test optimal representation agrees with a known analytic result.""" for ideal_gate, noise_level in product([CNOT, CZ], [0.1, 0.5]): q = LineQubit.range(2) ideal_op = Circuit(ideal_gate(*q)) implementable_circuits = [Circuit(ideal_op)] # Append two-qubit-gate with Paulis on one qubit for gate in [X, Y, Z]: implementable_circuits.append(Circuit([ideal_op, gate(q[0])])) implementable_circuits.append(Circuit([ideal_op, gate(q[1])])) # Append two-qubit gate with Paulis on both qubits for gate_a, gate_b in product([X, Y, Z], repeat=2): implementable_circuits.append( Circuit([ideal_op, gate_a(q[0]), gate_b(q[1])]) ) noisy_circuits = [ circ + Circuit(DepolarizingChannel(noise_level).on_each(*q)) for circ in implementable_circuits ] super_operators = [ choi_to_super(_circuit_to_choi(circ)) for circ in noisy_circuits ] # Define circuits with native types implementable_native = [ convert_from_mitiq(c, circ_type) for c in implementable_circuits ] ideal_op_native = convert_from_mitiq(ideal_op, circ_type) noisy_operations = [ NoisyOperation(ideal, real) for ideal, real in zip(implementable_native, super_operators) ] # Find optimal representation noisy_basis = NoisyBasis(*noisy_operations) rep = find_optimal_representation( ideal_op_native, noisy_basis, tol=1.0e-8 ) # Expected analytical result expected_rep = represent_operation_with_local_depolarizing_noise( ideal_op_native, noise_level, ) assert np.allclose(np.sort(rep.coeffs), np.sort(expected_rep.coeffs)) assert rep == expected_rep
def test_find_optimal_representation_single_qubit_amp_damping(circ_type): """Test optimal representation of agrees with a known analytic result.""" for ideal_gate, noise_level in product([X, Y, H], [0.1, 0.3]): q = LineQubit(0) ideal_op = Circuit(ideal_gate(q)) implementable_circuits = [Circuit(ideal_op)] # Add ideal gate followed by Paulis and reset for gate in [Z, reset]: implementable_circuits.append(Circuit([ideal_op, gate(q)])) noisy_circuits = [ circ + Circuit(AmplitudeDampingChannel(noise_level).on_each(q)) for circ in implementable_circuits ] super_operators = [ choi_to_super(_circuit_to_choi(circ)) for circ in noisy_circuits ] # Define circuits with native types implementable_native = [ convert_from_mitiq(c, circ_type) for c in implementable_circuits ] ideal_op_native = convert_from_mitiq(ideal_op, circ_type) noisy_operations = [ NoisyOperation(ideal, real) for ideal, real in zip(implementable_native, super_operators) ] # Find optimal representation noisy_basis = NoisyBasis(*noisy_operations) rep = find_optimal_representation( ideal_op_native, noisy_basis, tol=1.0e-7, initial_guess=[0, 0, 0] ) # Expected analytical result expected_rep = _represent_operation_with_amplitude_damping_noise( ideal_op_native, noise_level, ) assert np.allclose(np.sort(rep.coeffs), np.sort(expected_rep.coeffs)) assert rep == expected_rep
def test_non_squared_dimension(): with raises(ValueError, match="must be a square number"): vector_to_matrix(np.random.rand(7)) with raises(ValueError, match="must be a square number"): choi_to_super(np.random.rand(7, 7))