def test_channel():
a = cirq.NamedQubit('a')
op = cirq.bit_flip(0.5).on(a)
np.testing.assert_allclose(cirq.channel(op), cirq.channel(op.gate))
assert cirq.has_channel(op)
assert cirq.channel(cirq.SingleQubitGate()(a), None) is None
assert not cirq.has_channel(cirq.SingleQubitGate()(a))
def test_parameterized():
op = cirq.X.with_probability(sympy.Symbol('x'))
assert cirq.is_parameterized(op)
assert not cirq.has_channel(op)
assert not cirq.has_mixture(op)
op2 = cirq.resolve_parameters(op, {'x': 0.5})
assert op2 == cirq.X.with_probability(0.5)
assert not cirq.is_parameterized(op2)
assert cirq.has_channel(op2)
assert cirq.has_mixture(op2)
def test_asymmetric_depolarizing_channel():
d = cirq.asymmetric_depolarize(0.1, 0.2, 0.3)
np.testing.assert_almost_equal(
cirq.channel(d),
(np.sqrt(0.4) * np.eye(2), np.sqrt(0.1) * X, np.sqrt(0.2) * Y, np.sqrt(0.3) * Z),
)
assert cirq.has_channel(d)
def test_depolarizing_channel_two_qubits():
d = cirq.depolarize(0.15, n_qubits=2)
np.testing.assert_almost_equal(
cirq.channel(d),
(
np.sqrt(0.85) * np.eye(4),
np.sqrt(0.01) * np.kron(np.eye(2), X),
np.sqrt(0.01) * np.kron(np.eye(2), Y),
np.sqrt(0.01) * np.kron(np.eye(2), Z),
np.sqrt(0.01) * np.kron(X, np.eye(2)),
np.sqrt(0.01) * np.kron(X, X),
np.sqrt(0.01) * np.kron(X, Y),
np.sqrt(0.01) * np.kron(X, Z),
np.sqrt(0.01) * np.kron(Y, np.eye(2)),
np.sqrt(0.01) * np.kron(Y, X),
np.sqrt(0.01) * np.kron(Y, Y),
np.sqrt(0.01) * np.kron(Y, Z),
np.sqrt(0.01) * np.kron(Z, np.eye(2)),
np.sqrt(0.01) * np.kron(Z, X),
np.sqrt(0.01) * np.kron(Z, Y),
np.sqrt(0.01) * np.kron(Z, Z),
),
)
assert cirq.has_channel(d)
assert d.num_qubits() == 2
cirq.testing.assert_has_diagram(
cirq.Circuit(d(*cirq.LineQubit.range(2))),
"""
0: ───D(0.15)───
│
1: ───#2────────
""",
)
def test_phase_damping_channel():
d = cirq.phase_damp(0.3)
np.testing.assert_almost_equal(
cirq.channel(d), (np.array([[1.0, 0.], [0., np.sqrt(1 - 0.3)]]),
np.array([[0., 0.], [0., np.sqrt(0.3)]])))
assert cirq.has_channel(d)
assert not cirq.has_mixture(d)
def test_depolarizing_channel():
d = cirq.depolarize(0.3)
np.testing.assert_almost_equal(cirq.channel(d),
(np.sqrt(0.7) * np.eye(2),
np.sqrt(0.1) * X,
np.sqrt(0.1) * Y,
np.sqrt(0.1) * Z))
assert cirq.has_channel(d)
def test_generalized_amplitude_damping_channel():
d = cirq.generalized_amplitude_damp(0.1, 0.3)
np.testing.assert_almost_equal(cirq.channel(d),
(np.sqrt(0.1) * np.array([[1., 0.], [0., np.sqrt(1.-0.3)]]),
np.sqrt(0.1) * np.array([[0., np.sqrt(0.3)], [0., 0.]]),
np.sqrt(0.9) * np.array([[np.sqrt(1. - 0.3), 0.], [0., 1.]]),
np.sqrt(0.9) * np.array([[0., 0.], [np.sqrt(0.3), 0.]])))
assert cirq.has_channel(d)
assert not cirq.has_mixture_channel(d)
def test_has_channel():
class HasChannel:
def _has_channel_(self) -> bool:
return True
assert cirq.has_channel(HasChannel())
class HasMixture:
def _has_mixture_(self) -> bool:
return True
assert cirq.has_channel(HasMixture())
class HasUnitary:
def _has_unitary_(self) -> bool:
return True
assert cirq.has_channel(HasUnitary())
def test_reset_channel():
r = cirq.reset(cirq.LineQubit(0))
np.testing.assert_almost_equal(
cirq.channel(r),
(np.array([[1., 0.], [0., 0]]), np.array([[0., 1.], [0., 0.]])))
assert cirq.has_channel(r)
assert not cirq.has_mixture(r)
assert cirq.qid_shape(r) == (2, )
r = cirq.reset(cirq.LineQid(0, dimension=3))
np.testing.assert_almost_equal(
cirq.channel(r),
(np.array([[1, 0, 0], [0, 0, 0], [0, 0, 0]]),
np.array([[0, 1, 0], [0, 0, 0], [0, 0, 0]]),
np.array([[0, 0, 1], [0, 0, 0], [0, 0, 0]]))) # yapf: disable
assert cirq.has_channel(r)
assert not cirq.has_mixture(r)
assert cirq.qid_shape(r) == (3, )
def assert_not_implemented(val):
with pytest.raises(TypeError, match='returned NotImplemented'):
_ = cirq.channel(val)
assert cirq.channel(val, None) is None
assert cirq.channel(val, NotImplemented) is NotImplemented
assert cirq.channel(val, (1, )) == (1, )
assert cirq.channel(val, LOCAL_DEFAULT) is LOCAL_DEFAULT
assert not cirq.has_channel(val)
def test_amplitude_damping_channel():
d = cirq.amplitude_damp(0.3)
np.testing.assert_almost_equal(
cirq.channel(d),
(
np.array([[1.0, 0.0], [0.0, np.sqrt(1.0 - 0.3)]]),
np.array([[0.0, np.sqrt(0.3)], [0.0, 0.0]]),
),
)
assert cirq.has_channel(d)
assert not cirq.has_mixture(d)
def test_multi_asymmetric_depolarizing_channel():
d = cirq.asymmetric_depolarize(error_probabilities={'II': 0.8, 'XX': 0.2})
np.testing.assert_almost_equal(
cirq.channel(d),
(np.sqrt(0.8) * np.eye(4), np.sqrt(0.2) * np.kron(X, X)))
assert cirq.has_channel(d)
np.testing.assert_equal(d._num_qubits_(), 2)
with pytest.raises(ValueError, match="num_qubits should be 1"):
assert d.p_x == 0.0
with pytest.raises(ValueError, match="num_qubits should be 1"):
assert d.p_y == 0.0
with pytest.raises(ValueError, match="num_qubits should be 1"):
assert d.p_z == 0.0
def test_channel_no_methods():
class NoMethod:
pass
with pytest.raises(TypeError,
match='no _channel_ or _mixture_ or _unitary_ method'):
_ = cirq.channel(NoMethod())
assert cirq.channel(NoMethod(), None) is None
assert cirq.channel(NoMethod, NotImplemented) is NotImplemented
assert cirq.channel(NoMethod(), (1, )) == (1, )
assert cirq.channel(NoMethod(), LOCAL_DEFAULT) is LOCAL_DEFAULT
assert not cirq.has_channel(NoMethod())
def test_channel_propagates_to_gate():
class TestGate(cirq.SingleQubitGate):
def _channel_(self) -> np.ndarray:
return (np.eye(2), )
def _has_channel_(self) -> bool:
return True
def assert_kraus_eq(ks1: Tuple[np.ndarray, ...], ks2: Tuple[np.ndarray,
...]) -> None:
assert len(ks1) == len(ks2)
for k1, k2 in zip(ks1, ks2):
assert np.all(k1 == k2)
identity_kraus = (np.eye(2), )
q = cirq.LineQubit(0)
gate = TestGate()
gate_op = TestGate().on(q)
with cirq.testing.assert_deprecated(deadline='v0.13', count=None):
assert cirq.has_channel(gate)
assert cirq.has_channel(gate_op)
assert_kraus_eq(cirq.channel(gate), identity_kraus)
assert_kraus_eq(cirq.channel(gate_op), identity_kraus)
def test_channel_generates_deprecation_warning():
class UsesDeprecatedChannelMethod:
def _has_channel_(self):
return True
def _channel_(self):
return (np.eye(2), )
val = UsesDeprecatedChannelMethod()
with pytest.warns(DeprecationWarning, match='_has_kraus_'):
assert cirq.has_channel(val)
with pytest.warns(DeprecationWarning, match='_kraus_'):
ks = cirq.channel(val)
assert len(ks) == 1
assert np.all(ks[0] == np.eye(2))
def test_channel_fallback_to_unitary():
u = np.array([[1, 0], [1, 0]])
class ReturnsUnitary:
def _unitary_(self) -> np.ndarray:
return u
np.testing.assert_equal(cirq.channel(ReturnsUnitary()), (u, ))
np.testing.assert_equal(cirq.channel(ReturnsUnitary(), None), (u, ))
np.testing.assert_equal(cirq.channel(ReturnsUnitary(), NotImplemented),
(u, ))
np.testing.assert_equal(cirq.channel(ReturnsUnitary(), (1, )), (u, ))
np.testing.assert_equal(cirq.channel(ReturnsUnitary(), LOCAL_DEFAULT),
(u, ))
assert cirq.has_channel(ReturnsUnitary())
def test_channel():
a0 = np.array([[0, 0], [1, 0]])
a1 = np.array([[1, 0], [0, 0]])
c = (a0, a1)
class ReturnsChannel:
def _channel_(self) -> Sequence[np.ndarray]:
return c
assert cirq.channel(ReturnsChannel()) is c
assert cirq.channel(ReturnsChannel(), None) is c
assert cirq.channel(ReturnsChannel(), NotImplemented) is c
assert cirq.channel(ReturnsChannel(), (1, )) is c
assert cirq.channel(ReturnsChannel(), LOCAL_DEFAULT) is c
assert cirq.has_channel(ReturnsChannel())
def test_channel_fallback_to_mixture():
m = ((0.3, cirq.unitary(cirq.X)), (0.4, cirq.unitary(cirq.Y)),
(0.3, cirq.unitary(cirq.Z)))
class ReturnsMixture:
def _mixture_(self) -> Iterable[Tuple[float, np.ndarray]]:
return m
c = (np.sqrt(0.3) * cirq.unitary(cirq.X),
np.sqrt(0.4) * cirq.unitary(cirq.Y),
np.sqrt(0.3) * cirq.unitary(cirq.Z))
np.allclose(cirq.channel(ReturnsMixture()), c)
np.allclose(cirq.channel(ReturnsMixture(), None), c)
np.allclose(cirq.channel(ReturnsMixture(), NotImplemented), c)
np.allclose(cirq.channel(ReturnsMixture(), (1, )), c)
np.allclose(cirq.channel(ReturnsMixture(), LOCAL_DEFAULT), c)
assert cirq.has_channel(ReturnsMixture())
def test_depolarizing_channel_two_qubits():
d = cirq.depolarize(0.15, n_qubits=2)
np.testing.assert_almost_equal(cirq.channel(d), (
np.sqrt(0.85) * np.eye(4),
np.sqrt(0.01) * np.kron(np.eye(2), X),
np.sqrt(0.01) * np.kron(np.eye(2), Y),
np.sqrt(0.01) * np.kron(np.eye(2), Z),
np.sqrt(0.01) * np.kron(X, np.eye(2)),
np.sqrt(0.01) * np.kron(X, X),
np.sqrt(0.01) * np.kron(X, Y),
np.sqrt(0.01) * np.kron(X, Z),
np.sqrt(0.01) * np.kron(Y, np.eye(2)),
np.sqrt(0.01) * np.kron(Y, X),
np.sqrt(0.01) * np.kron(Y, Y),
np.sqrt(0.01) * np.kron(Y, Z),
np.sqrt(0.01) * np.kron(Z, np.eye(2)),
np.sqrt(0.01) * np.kron(Z, X),
np.sqrt(0.01) * np.kron(Z, Y),
np.sqrt(0.01) * np.kron(Z, Z),
))
assert cirq.has_channel(d)
def test_reset_channel():
np.testing.assert_almost_equal(
cirq.channel(cirq.RESET),
(np.array([[1., 0.], [0., 0]]), np.array([[0., 1.], [0., 0.]])))
assert cirq.has_channel(cirq.RESET)
assert not cirq.has_mixture_channel(cirq.RESET)
def test_tagged_operation_forwards_protocols():
"""The results of all protocols applied to an operation with a tag should
be equivalent to the result without tags.
"""
q1 = cirq.GridQubit(1, 1)
q2 = cirq.GridQubit(1, 2)
h = cirq.H(q1)
tag = 'tag1'
tagged_h = cirq.H(q1).with_tags(tag)
np.testing.assert_equal(cirq.unitary(tagged_h), cirq.unitary(h))
assert cirq.has_unitary(tagged_h)
assert cirq.decompose(tagged_h) == cirq.decompose(h)
assert cirq.pauli_expansion(tagged_h) == cirq.pauli_expansion(h)
assert cirq.equal_up_to_global_phase(h, tagged_h)
assert np.isclose(cirq.channel(h), cirq.channel(tagged_h)).all()
assert cirq.measurement_key(cirq.measure(q1, key='blah').with_tags(tag)) == 'blah'
parameterized_op = cirq.XPowGate(exponent=sympy.Symbol('t'))(q1).with_tags(tag)
assert cirq.is_parameterized(parameterized_op)
resolver = cirq.study.ParamResolver({'t': 0.25})
assert cirq.resolve_parameters(parameterized_op, resolver) == cirq.XPowGate(exponent=0.25)(
q1
).with_tags(tag)
assert cirq.resolve_parameters_once(parameterized_op, resolver) == cirq.XPowGate(exponent=0.25)(
q1
).with_tags(tag)
y = cirq.Y(q1)
tagged_y = cirq.Y(q1).with_tags(tag)
assert tagged_y ** 0.5 == cirq.YPowGate(exponent=0.5)(q1)
assert tagged_y * 2 == (y * 2)
assert 3 * tagged_y == (3 * y)
assert cirq.phase_by(y, 0.125, 0) == cirq.phase_by(tagged_y, 0.125, 0)
controlled_y = tagged_y.controlled_by(q2)
assert controlled_y.qubits == (
q2,
q1,
)
assert isinstance(controlled_y, cirq.Operation)
assert not isinstance(controlled_y, cirq.TaggedOperation)
clifford_x = cirq.SingleQubitCliffordGate.X(q1)
tagged_x = cirq.SingleQubitCliffordGate.X(q1).with_tags(tag)
assert cirq.commutes(clifford_x, clifford_x)
assert cirq.commutes(tagged_x, clifford_x)
assert cirq.commutes(clifford_x, tagged_x)
assert cirq.commutes(tagged_x, tagged_x)
assert cirq.trace_distance_bound(y ** 0.001) == cirq.trace_distance_bound(
(y ** 0.001).with_tags(tag)
)
flip = cirq.bit_flip(0.5)(q1)
tagged_flip = cirq.bit_flip(0.5)(q1).with_tags(tag)
assert cirq.has_mixture(tagged_flip)
assert cirq.has_channel(tagged_flip)
flip_mixture = cirq.mixture(flip)
tagged_mixture = cirq.mixture(tagged_flip)
assert len(tagged_mixture) == 2
assert len(tagged_mixture[0]) == 2
assert len(tagged_mixture[1]) == 2
assert tagged_mixture[0][0] == flip_mixture[0][0]
assert np.isclose(tagged_mixture[0][1], flip_mixture[0][1]).all()
assert tagged_mixture[1][0] == flip_mixture[1][0]
assert np.isclose(tagged_mixture[1][1], flip_mixture[1][1]).all()
qubit_map = {q1: 'q1'}
qasm_args = cirq.QasmArgs(qubit_id_map=qubit_map)
assert cirq.qasm(h, args=qasm_args) == cirq.qasm(tagged_h, args=qasm_args)
cirq.testing.assert_has_consistent_apply_unitary(tagged_h)
def translate_cirq_to_qtrajectory(
self,
qubit_order: cirq.ops.QubitOrderOrList = cirq.ops.QubitOrder.DEFAULT
) -> qsim.NoisyCircuit:
"""
Translates this noisy Cirq circuit to the qsim representation.
:qubit_order: Ordering of qubits
:return: a C++ qsim NoisyCircuit object
"""
qsim_ncircuit = qsim.NoisyCircuit()
ordered_qubits = cirq.ops.QubitOrder.as_qubit_order(
qubit_order).order_for(self.all_qubits())
# qsim numbers qubits in reverse order from cirq
ordered_qubits = list(reversed(ordered_qubits))
qsim_ncircuit.num_qubits = len(ordered_qubits)
def has_qsim_kind(op: cirq.ops.GateOperation):
return _cirq_gate_kind(op.gate) != None
def to_matrix(op: cirq.ops.GateOperation):
mat = cirq.unitary(op.gate, None)
if mat is None:
return NotImplemented
return cirq.ops.MatrixGate(mat).on(*op.qubits)
qubit_to_index_dict = {q: i for i, q in enumerate(ordered_qubits)}
time_offset = 0
for moment in self:
moment_length = 0
ops_by_gate = []
ops_by_mix = []
ops_by_channel = []
# Capture ops of each type in the appropriate list.
for qsim_op in moment:
if cirq.has_unitary(qsim_op) or cirq.is_measurement(qsim_op):
oplist = cirq.decompose(qsim_op,
fallback_decomposer=to_matrix,
keep=has_qsim_kind)
ops_by_gate.append(oplist)
moment_length = max(moment_length, len(oplist))
pass
elif cirq.has_mixture(qsim_op):
ops_by_mix.append(qsim_op)
moment_length = max(moment_length, 1)
pass
elif cirq.has_channel(qsim_op):
ops_by_channel.append(qsim_op)
moment_length = max(moment_length, 1)
pass
else:
raise ValueError(f'Encountered unparseable op: {qsim_op}')
# Gates must be added in time order.
for gi in range(moment_length):
# Handle gate output.
for gate_ops in ops_by_gate:
if gi >= len(gate_ops):
continue
qsim_op = gate_ops[gi]
time = time_offset + gi
gate_kind = _cirq_gate_kind(qsim_op.gate)
add_op_to_circuit(qsim_op, time, qubit_to_index_dict,
qsim_ncircuit)
# Handle mixture output.
for mixture in ops_by_mix:
mixdata = []
for prob, mat in cirq.mixture(mixture):
square_mat = np.reshape(mat,
(int(np.sqrt(mat.size)), -1))
unitary = cirq.is_unitary(square_mat)
# Package matrix into a qsim-friendly format.
mat = np.reshape(mat, (-1, )).astype(np.complex64,
copy=False)
mixdata.append((prob, mat.view(np.float32), unitary))
qubits = [qubit_to_index_dict[q] for q in mixture.qubits]
qsim.add_channel(time_offset, qubits, mixdata,
qsim_ncircuit)
# Handle channel output.
for channel in ops_by_channel:
chdata = []
for i, mat in enumerate(cirq.channel(channel)):
square_mat = np.reshape(mat,
(int(np.sqrt(mat.size)), -1))
unitary = cirq.is_unitary(square_mat)
singular_vals = np.linalg.svd(square_mat)[1]
lower_bound_prob = min(singular_vals)**2
# Package matrix into a qsim-friendly format.
mat = np.reshape(mat, (-1, )).astype(np.complex64,
copy=False)
chdata.append(
(lower_bound_prob, mat.view(np.float32), unitary))
qubits = [qubit_to_index_dict[q] for q in channel.qubits]
qsim.add_channel(time_offset, qubits, chdata,
qsim_ncircuit)
time_offset += moment_length
return qsim_ncircuit
def test_bit_flip_channel():
d = cirq.bit_flip(0.3)
np.testing.assert_almost_equal(
cirq.channel(d), (np.sqrt(1.0 - 0.3) * np.eye(2), np.sqrt(0.3) * X))
assert cirq.has_channel(d)
def test_has_channel_protocol_is_deprecated():
with cirq.testing.assert_deprecated(deadline='v0.13'):
assert cirq.has_channel(cirq.depolarize(0.1)) == cirq.has_kraus(cirq.depolarize(0.1))
def test_has_channel_when_decomposed(decomposed_cls):
op = HasChannelWhenDecomposed(decomposed_cls).on(cirq.NamedQubit('test'))
assert cirq.has_channel(op)
assert not cirq.has_channel(op, allow_decompose=False)
def test_has_channel(cls):
assert cirq.has_channel(cls())
def circuit_to_density_matrix_tensors(
circuit: cirq.Circuit,
qubits: Optional[Sequence[cirq.LineQubit]] = None
) -> Tuple[List[qtn.Tensor], Dict['cirq.Qid', int], Dict[Tuple[str, str],
Tuple[float, float]]]:
"""Given a circuit with mixtures or channels, construct a tensor network
representation of the density matrix.
This assumes you start in the |0..0><0..0| state. Indices are named
"nf{i}_q{x}" and "nb{i}_q{x}" where i is a time index and x is a
qubit index. nf- and nb- refer to the "forwards" and "backwards"
copies of the circuit. Kraus indices are named "k{j}" where j is an
independent "kraus" internal index which you probably never need to access.
Args:
circuit: The circuit containing operations that support the
cirq.unitary() or cirq.channel() protocols.
qubits: The qubits in the circuit.
Returns:
tensors: A list of Quimb Tensor objects
qubit_frontier: A mapping from qubit to time index at the end of
the circuit. This can be used to deduce the names of the free
tensor indices.
positions: A positions dictionary suitable for passing to tn.graph()'s
`fix` argument to draw the resulting tensor network similar to a
quantum circuit.
"""
if qubits is None:
# coverage: ignore
qubits = sorted(cast(Iterable[cirq.LineQubit], circuit.all_qubits()))
qubit_frontier: Dict[cirq.Qid, int] = {q: 0 for q in qubits}
kraus_frontier = 0
positions: Dict[Tuple[str, str], Tuple[float, float]] = {}
tensors: List[qtn.Tensor] = []
x_scale = 2
y_scale = 3
x_nudge = 0.3
n_qubits = len(qubits)
yb_offset = (n_qubits + 0.5) * y_scale
def _positions(mi, qubits):
return _add_to_positions(
positions,
mi,
qubits,
tot_n_qubits=n_qubits,
x_scale=x_scale,
y_scale=y_scale,
x_nudge=x_nudge,
yb_offset=yb_offset,
)
# Initialize forwards and backwards qubits into the 0 state, i.e. prepare
# rho_0 = |0><0|.
for q in qubits:
tensors += [
qtn.Tensor(data=quimb.up().squeeze(),
inds=(f'nf0_q{q.x}', ),
tags={'Q0', 'i0f', _qpos_tag(q)}),
qtn.Tensor(data=quimb.up().squeeze(),
inds=(f'nb0_q{q.x}', ),
tags={'Q0', 'i0b', _qpos_tag(q)}),
]
_positions(0, q)
for mi, moment in enumerate(circuit.moments):
for op in moment.operations:
start_inds_f = [f'nf{qubit_frontier[q]}_q{q.x}' for q in op.qubits]
start_inds_b = [f'nb{qubit_frontier[q]}_q{q.x}' for q in op.qubits]
for q in op.qubits:
qubit_frontier[q] += 1
end_inds_f = [f'nf{qubit_frontier[q]}_q{q.x}' for q in op.qubits]
end_inds_b = [f'nb{qubit_frontier[q]}_q{q.x}' for q in op.qubits]
if cirq.has_unitary(op):
U = cirq.unitary(op).reshape(
(2, ) * 2 * len(op.qubits)).astype(np.complex128)
tensors.append(
qtn.Tensor(
data=U,
inds=end_inds_f + start_inds_f,
tags={
f'Q{len(op.qubits)}', f'i{mi + 1}f',
_qpos_tag(op.qubits)
},
))
tensors.append(
qtn.Tensor(
data=np.conj(U),
inds=end_inds_b + start_inds_b,
tags={
f'Q{len(op.qubits)}', f'i{mi + 1}b',
_qpos_tag(op.qubits)
},
))
elif cirq.has_channel(op):
K = np.asarray(cirq.channel(op), dtype=np.complex128)
kraus_inds = [f'k{kraus_frontier}']
tensors.append(
qtn.Tensor(
data=K,
inds=kraus_inds + end_inds_f + start_inds_f,
tags={
f'kQ{len(op.qubits)}', f'i{mi + 1}f',
_qpos_tag(op.qubits)
},
))
tensors.append(
qtn.Tensor(
data=np.conj(K),
inds=kraus_inds + end_inds_b + start_inds_b,
tags={
f'kQ{len(op.qubits)}', f'i{mi + 1}b',
_qpos_tag(op.qubits)
},
))
kraus_frontier += 1
else:
raise ValueError(repr(op)) # coverage: ignore
_positions(mi + 1, op.qubits)
return tensors, qubit_frontier, positions
def test_phase_flip_channel():
d = cirq.phase_flip(0.3)
np.testing.assert_almost_equal(
cirq.channel(d), (np.sqrt(1. - 0.3) * np.eye(2), np.sqrt(0.3) * Z))
assert cirq.has_channel(d)
def test_channel():
class NoDetailsGate(cirq.Gate):
def num_qubits(self) -> int:
return 1
assert not cirq.has_channel(NoDetailsGate().with_probability(0.5))
assert cirq.channel(NoDetailsGate().with_probability(0.5), None) is None
assert cirq.channel(cirq.X.with_probability(sympy.Symbol('x')),
None) is None
assert_channel_sums_to_identity(cirq.X.with_probability(0.25))
assert_channel_sums_to_identity(cirq.bit_flip(0.75).with_probability(0.25))
assert_channel_sums_to_identity(
cirq.amplitude_damp(0.75).with_probability(0.25))
m = cirq.channel(cirq.X.with_probability(0.25))
assert len(m) == 2
np.testing.assert_allclose(
m[0],
cirq.unitary(cirq.X) * np.sqrt(0.25),
atol=1e-8,
)
np.testing.assert_allclose(
m[1],
cirq.unitary(cirq.I) * np.sqrt(0.75),
atol=1e-8,
)
m = cirq.channel(cirq.bit_flip(0.75).with_probability(0.25))
assert len(m) == 3
np.testing.assert_allclose(
m[0],
cirq.unitary(cirq.I) * np.sqrt(0.25) * np.sqrt(0.25),
atol=1e-8,
)
np.testing.assert_allclose(
m[1],
cirq.unitary(cirq.X) * np.sqrt(0.25) * np.sqrt(0.75),
atol=1e-8,
)
np.testing.assert_allclose(
m[2],
cirq.unitary(cirq.I) * np.sqrt(0.75),
atol=1e-8,
)
m = cirq.channel(cirq.amplitude_damp(0.75).with_probability(0.25))
assert len(m) == 3
np.testing.assert_allclose(
m[0],
np.array([[1, 0], [0, np.sqrt(1 - 0.75)]]) * np.sqrt(0.25),
atol=1e-8,
)
np.testing.assert_allclose(
m[1],
np.array([[0, np.sqrt(0.75)], [0, 0]]) * np.sqrt(0.25),
atol=1e-8,
)
np.testing.assert_allclose(
m[2],
cirq.unitary(cirq.I) * np.sqrt(0.75),
atol=1e-8,
)
