def test_circuit_light_cone():
from qibo import __version__
nqubits = 10
c = Circuit(nqubits)
c.add(gates.RY(i, theta=0) for i in range(nqubits))
c.add(gates.CZ(i, i + 1) for i in range(0, nqubits - 1, 2))
c.add(gates.RY(i, theta=0) for i in range(nqubits))
c.add(gates.CZ(i, i + 1) for i in range(1, nqubits - 1, 2))
c.add(gates.CZ(0, nqubits - 1))
sc, qubit_map = c.light_cone(4, 5)
target_qasm = f"""// Generated by QIBO {__version__}
OPENQASM 2.0;
include "qelib1.inc";
qreg q[6];
ry(0) q[0];
ry(0) q[1];
ry(0) q[2];
ry(0) q[3];
ry(0) q[4];
ry(0) q[5];
cz q[0],q[1];
cz q[2],q[3];
cz q[4],q[5];
ry(0) q[1];
ry(0) q[2];
ry(0) q[3];
ry(0) q[4];
cz q[1],q[2];
cz q[3],q[4];"""
assert qubit_map == {2: 0, 3: 1, 4: 2, 5: 3, 6: 4, 7: 5}
assert sc.to_qasm() == target_qasm
def test_cnot_and_cz_init():
gate = gates.CNOT(0, 1)
assert gate.target_qubits == (1,)
assert gate.control_qubits == (0,)
gate = gates.CZ(3, 2)
assert gate.target_qubits == (2,)
assert gate.control_qubits == (3,)
def test_circuit_set_parameters_with_dictionary(trainable):
"""Check updating parameters of circuit with list."""
params = [0.123, 0.456, 0.789]
c1 = Circuit(3)
c1.add(gates.X(0))
c1.add(gates.X(2))
if trainable:
c1.add(gates.U1(0, theta=0, trainable=trainable))
else:
c1.add(gates.U1(0, theta=params[0], trainable=trainable))
c2 = Circuit(3)
c2.add(gates.RZ(1, theta=0))
c2.add(gates.CZ(1, 2))
c2.add(gates.CU1(0, 2, theta=0))
c2.add(gates.H(2))
c = c1 + c2
if trainable:
params_dict = {c.queue[i]: p for i, p in zip([2, 3, 5], params)}
c.set_parameters(params_dict)
assert c.queue[2].parameters == params[0]
else:
params_dict = {c.queue[3]: params[1], c.queue[5]: params[2]}
c.set_parameters(params_dict)
assert c.queue[3].parameters == params[1]
assert c.queue[5].parameters == params[2]
# test not passing all parametrized gates
c.set_parameters({c.queue[5]: 0.7891})
if trainable:
assert c.queue[2].parameters == params[0]
assert c.queue[3].parameters == params[1]
assert c.queue[5].parameters == 0.7891
def test_circuit_on_qubits():
c = Circuit(3)
c.add([gates.H(0), gates.X(1), gates.Y(2)])
c.add([gates.CNOT(0, 1), gates.CZ(1, 2)])
c.add(gates.H(1).controlled_by(0, 2))
new_gates = list(c.on_qubits(2, 5, 4))
assert new_gates[0].target_qubits == (2, )
assert new_gates[1].target_qubits == (5, )
assert new_gates[2].target_qubits == (4, )
assert new_gates[3].target_qubits == (5, )
assert new_gates[3].control_qubits == (2, )
assert new_gates[4].target_qubits == (4, )
assert new_gates[4].control_qubits == (5, )
assert new_gates[5].target_qubits == (5, )
assert new_gates[5].control_qubits == (2, 4)
def test_circuit_invert(measurements):
c = Circuit(3)
gatelist = [gates.H(0), gates.X(1), gates.Y(2),
gates.CNOT(0, 1), gates.CZ(1, 2)]
c.add(gatelist)
if measurements:
c.add(gates.M(0, 2))
invc = c.invert()
for g1, g2 in zip(invc.queue, gatelist[::-1]):
g2 = g2.dagger()
assert isinstance(g1, g2.__class__)
assert g1.target_qubits == g2.target_qubits
assert g1.control_qubits == g2.control_qubits
if measurements:
assert invc.measurement_gate.target_qubits == (0, 2)
assert invc.measurement_tuples == {"register0": (0, 2)}
def test_circuit_decompose(measurements):
c = Circuit(4)
c.add([gates.H(0), gates.X(1), gates.Y(2)])
c.add([gates.CZ(0, 1), gates.CNOT(2, 3), gates.TOFFOLI(0, 1, 3)])
if measurements:
c.add(gates.M(0, 2))
decompc = c.decompose()
dgates = []
for gate in c.queue:
dgates.extend(gate.decompose())
for g1, g2 in zip(decompc.queue, dgates):
assert isinstance(g1, g2.__class__)
assert g1.target_qubits == g2.target_qubits
assert g1.control_qubits == g2.control_qubits
if measurements:
assert decompc.measurement_gate.target_qubits == (0, 2)
assert decompc.measurement_tuples == {"register0": (0, 2)}
def test_circuit_set_parameters_with_list(trainable):
"""Check updating parameters of circuit with list."""
params = [0.123, 0.456, (0.789, 0.321)]
c = Circuit(3)
if trainable:
c.add(gates.RX(0, theta=0, trainable=trainable))
else:
c.add(gates.RX(0, theta=params[0], trainable=trainable))
c.add(gates.RY(1, theta=0))
c.add(gates.CZ(1, 2))
c.add(gates.fSim(0, 2, theta=0, phi=0))
c.add(gates.H(2))
if trainable:
c.set_parameters(params)
assert c.queue[0].parameters == params[0]
else:
c.set_parameters(params[1:])
assert c.queue[1].parameters == params[1]
assert c.queue[3].parameters == params[2]
def test_get_parameters(trainable, include_not_trainable, format):
import numpy as np
matrix = np.random.random((2, 2))
c = Circuit(3)
c.add(gates.RX(0, theta=0.123))
c.add(gates.RY(1, theta=0.456, trainable=trainable))
c.add(gates.CZ(1, 2))
c.add(gates.Unitary(matrix, 2))
c.add(gates.fSim(0, 2, theta=0.789, phi=0.987, trainable=trainable))
c.add(gates.H(2))
c.param_tensor_types = (np.ndarray, )
params = c.get_parameters(format, include_not_trainable)
if trainable or include_not_trainable:
target_params = {
"list": [0.123, 0.456, (0.789, 0.987)],
"dict": {
c.queue[0]: 0.123,
c.queue[1]: 0.456,
c.queue[4]: (0.789, 0.987)
},
"flatlist": [0.123, 0.456]
}
target_params["flatlist"].extend(list(matrix.ravel()))
target_params["flatlist"].extend([0.789, 0.987])
else:
target_params = {
"list": [0.123],
"dict": {
c.queue[0]: 0.123
},
"flatlist": [0.123]
}
target_params["flatlist"].extend(list(matrix.ravel()))
if format == "list":
i = len(target_params["list"]) // 2 + 1
np.testing.assert_allclose(params.pop(i), matrix)
elif format == "dict":
np.testing.assert_allclose(params.pop(c.queue[3]), matrix)
assert params == target_params[format]
with pytest.raises(ValueError):
c.get_parameters("test")
def test_fused_gate():
gate = gates.FusedGate(0, 1)
gate.add(gates.H(0))
gate.add(gates.CNOT(0, 1))
with pytest.raises(ValueError):
gate.add(gates.CZ(1, 2))
