def test_noise_model(): km1 = KrausModel('I', (5., ), (0, 1), [np.array([[1 + 1j]])], 1.0) km2 = KrausModel('RX', (np.pi / 2, ), (0, ), [np.array([[1 + 1j]])], 1.0) nm = NoiseModel([km1, km2], {0: np.eye(2), 1: np.eye(2)}) assert nm == NoiseModel.from_dict(nm.to_dict()) assert nm.gates_by_name("I") == [km1] assert nm.gates_by_name("RX") == [km2]
def test_kraus_model(): km = KrausModel('I', (5., ), (0, 1), [np.array([[1 + 1j]])], 1.0) d = km.to_dict() assert d == OrderedDict([('gate', km.gate), ('params', km.params), ('targets', (0, 1)), ('kraus_ops', [[[[1.]], [[1.0]]]]), ('fidelity', 1.0)]) assert KrausModel.from_dict(d) == km
def test_kraus_model(): km = KrausModel("I", (5.0, ), (0, 1), [np.array([[1 + 1j]])], 1.0) d = km.to_dict() assert d == OrderedDict([ ("gate", km.gate), ("params", km.params), ("targets", (0, 1)), ("kraus_ops", [[[[1.0]], [[1.0]]]]), ("fidelity", 1.0), ]) assert KrausModel.from_dict(d) == km
def test_kraus_model(kraus_model_I_dict): km = KrausModel.from_dict(kraus_model_I_dict) assert km == KrausModel(gate=kraus_model_I_dict['gate'], params=kraus_model_I_dict['params'], targets=kraus_model_I_dict['targets'], kraus_ops=[ KrausModel.unpack_kraus_matrix(kraus_op) for kraus_op in kraus_model_I_dict['kraus_ops'] ], fidelity=kraus_model_I_dict['fidelity']) d = km.to_dict() assert d == OrderedDict([('gate', km.gate), ('params', km.params), ('targets', (0, 1)), ('kraus_ops', [[[[1.]], [[1.0]]]]), ('fidelity', 1.0)])
def test_kraus_model_2(kraus_model_I_dict): km = KrausModel.from_dict(kraus_model_I_dict) assert km == KrausModel( gate=kraus_model_I_dict["gate"], params=kraus_model_I_dict["params"], targets=kraus_model_I_dict["targets"], kraus_ops=[ KrausModel.unpack_kraus_matrix(kraus_op) for kraus_op in kraus_model_I_dict["kraus_ops"] ], fidelity=kraus_model_I_dict["fidelity"], ) d = km.to_dict() assert d == OrderedDict([ ("gate", km.gate), ("params", km.params), ("targets", (0, 1)), ("kraus_ops", [[[[1.0]], [[1.0]]]]), ("fidelity", 1.0), ])
def _modified_decoherence_noise_model( gates: Sequence[Gate], T1: Union[Dict[int, float], float] = 30e-6, T2: Union[Dict[int, float], float] = 30e-6, gate_time_1q: float = 50e-9, gate_time_2q: float = 150e-09, ro_fidelity: Union[Dict[int, float], float] = 0.95, ) -> NoiseModel: """ The default noise parameters - T1 = 30 us - T2 = 30 us - 1q gate time = 50 ns - 2q gate time = 150 ns are currently typical for near-term devices. This function will define new gates and add Kraus noise to these gates. It will translate the input program to use the noisy version of the gates. :param gates: The gates to provide the noise model for. :param T1: The T1 amplitude damping time either globally or in a dictionary indexed by qubit id. By default, this is 30 us. :param T2: The T2 dephasing time either globally or in a dictionary indexed by qubit id. By default, this is also 30 us. :param gate_time_1q: The duration of the one-qubit gates, namely RX(+pi/2) and RX(-pi/2). By default, this is 50 ns. :param gate_time_2q: The duration of the two-qubit gates, namely CZ. By default, this is 150 ns. :param ro_fidelity: The readout assignment fidelity :math:`F = (p(0|0) + p(1|1))/2` either globally or in a dictionary indexed by qubit id. :return: A NoiseModel with the appropriate Kraus operators defined. """ all_qubits = set(sum(([t.index for t in g.qubits] for g in gates), [])) if isinstance(T1, dict): all_qubits.update(T1.keys()) if isinstance(T2, dict): all_qubits.update(T2.keys()) if isinstance(ro_fidelity, dict): all_qubits.update(ro_fidelity.keys()) if not isinstance(T1, dict): T1 = {q: T1 for q in all_qubits} if not isinstance(T2, dict): T2 = {q: T2 for q in all_qubits} if not isinstance(ro_fidelity, dict): ro_fidelity = {q: ro_fidelity for q in all_qubits} kraus_maps = [] for g in gates: targets = tuple(t.index for t in g.qubits) key = (g.name, tuple(g.params)) if g.name in NO_NOISE: if not g.dd: g.gate_time = gate_time_1q continue matrix, _ = get_modified_noisy_gate(g.name, g.params) if len(targets) == 1: if g.gate_time == None: g.gate_time = gate_time_1q noisy_I = damping_after_dephasing(T1.get(targets[0], INFINITY), T2.get(targets[0], INFINITY), g.gate_time) else: if len(targets) != 2: raise ValueError( "Noisy gates on more than 2Q not currently supported") if g.gate_time == None: g.gate_time = gate_time_2q # note this ordering of the tensor factors is necessary due to how the QVM orders # the wavefunction basis noisy_I = tensor_kraus_maps( damping_after_dephasing(T1.get(targets[1], INFINITY), T2.get(targets[1], INFINITY), g.gate_time), damping_after_dephasing(T1.get(targets[0], INFINITY), T2.get(targets[0], INFINITY), g.gate_time)) kraus_maps.append( KrausModel(g.name, tuple(g.params), targets, combine_kraus_maps(noisy_I, [matrix]), 1.0)) aprobs = {} for q, f_ro in ro_fidelity.items(): aprobs[q] = np.array([[f_ro, 1. - f_ro], [1. - f_ro, f_ro]]) return NoiseModel(kraus_maps, aprobs)