def testresolve(self, gate_from, gate_to, targets, controls):
qc1 = QubitCircuit(2)
qc1.add_gate(gate_from, targets=targets, controls=controls)
U1 = gates.gate_sequence_product(qc1.propagators())
qc2 = qc1.resolve_gates(basis=gate_to)
U2 = gates.gate_sequence_product(qc2.propagators())
assert _op_dist(U1, U2) < 1e-12
def test_numerical_evolution(num_qubits, gates, device_class, kwargs):
num_qubits = 2
circuit = QubitCircuit(num_qubits)
for gate in gates:
circuit.add_gate(gate)
device = device_class(num_qubits, **kwargs)
device.load_circuit(circuit)
state = qutip.rand_ket(2**num_qubits)
state.dims = [[2] * num_qubits, [1] * num_qubits]
target = gate_sequence_product([state] + circuit.propagators())
if isinstance(device, DispersiveCavityQED):
num_ancilla = len(device.dims) - num_qubits
ancilla_indices = slice(0, num_ancilla)
extra = qutip.basis(device.dims[ancilla_indices], [0] * num_ancilla)
init_state = qutip.tensor(extra, state)
elif isinstance(device, SCQubits):
# expand to 3-level represetnation
init_state = _ket_expaned_dims(state, device.dims)
else:
init_state = state
options = qutip.Options(store_final_state=True, nsteps=50_000)
result = device.run_state(init_state=init_state,
analytical=False,
options=options)
numerical_result = result.final_state
if isinstance(device, DispersiveCavityQED):
target = qutip.tensor(extra, target)
elif isinstance(device, SCQubits):
target = _ket_expaned_dims(target, device.dims)
assert _tol > abs(1 - qutip.metrics.fidelity(numerical_result, target))
def test_simple_hadamard(self):
"""
Test for optimizing a simple hadamard gate
"""
N = 1
H_d = sigmaz()
H_c = sigmax()
qc = QubitCircuit(N)
qc.add_gate("SNOT", 0)
# test load_circuit, with verbose info
num_tslots = 10
evo_time = 10
test = OptPulseProcessor(N, drift=H_d)
test.add_control(H_c, targets=0)
tlist, coeffs = test.load_circuit(qc,
num_tslots=num_tslots,
evo_time=evo_time,
verbose=True)
# test run_state
rho0 = qubit_states(1, [0])
plus = (qubit_states(1, [0]) + qubit_states(1, [1])).unit()
result = test.run_state(rho0)
assert_allclose(fidelity(result.states[-1], plus), 1, rtol=1.0e-6)
def test_N_level_system(self):
"""
Test for circuit with N-level system.
"""
mat3 = qp.rand_unitary_haar(3)
def controlled_mat3(arg_value):
"""
A qubit control an operator acting on a 3 level system
"""
control_value = arg_value
dim = mat3.dims[0][0]
return (tensor(fock_dm(2, control_value), mat3) +
tensor(fock_dm(2, 1 - control_value), identity(dim)))
qc = QubitCircuit(2, dims=[3, 2])
qc.user_gates = {"CTRLMAT3": controlled_mat3}
qc.add_gate("CTRLMAT3", targets=[1, 0], arg_value=1)
props = qc.propagators()
final_fid = qp.average_gate_fidelity(mat3, ptrace(props[0], 0) - 1)
assert pytest.approx(final_fid, 1.0e-6) == 1
init_state = basis([3, 2], [0, 1])
result = qc.run(init_state)
final_fid = qp.fidelity(result, props[0] * init_state)
assert pytest.approx(final_fid, 1.0e-6) == 1.
def test_scheduling_pulse(instructions, method, expected_length,
random_shuffle, gates_schedule):
circuit = QubitCircuit(4)
for instruction in instructions:
circuit.add_gate(
Gate(instruction.name, instruction.targets, instruction.controls))
if random_shuffle:
repeat_num = 5
else:
repeat_num = 0
result0 = gate_sequence_product(circuit.propagators())
# run the scheduler
scheduler = Scheduler(method)
gate_cycle_indices = scheduler.schedule(instructions,
gates_schedule=gates_schedule,
repeat_num=repeat_num)
# check if the scheduled length is expected
assert (max(gate_cycle_indices) == expected_length)
scheduled_gate = [[] for i in range(max(gate_cycle_indices) + 1)]
# check if the scheduled circuit is correct
for i, cycles in enumerate(gate_cycle_indices):
scheduled_gate[cycles].append(circuit.gates[i])
circuit.gates = sum(scheduled_gate, [])
result1 = gate_sequence_product(circuit.propagators())
assert (tracedist(result0 * result1.dag(), qeye(result0.dims[0])) < 1.0e-7)
def test_compiling_with_scheduler():
"""
Here we test if the compiling with scheduler works properly.
The non scheduled pulse should be twice as long as the scheduled one.
The numerical results are tested in test_device.py
"""
circuit = QubitCircuit(2)
circuit.add_gate("X", 0)
circuit.add_gate("X", 1)
processor = DispersiveCavityQED(2)
processor.load_circuit(circuit, schedule_mode=None)
tlist = processor.get_full_tlist()
time_not_scheduled = tlist[-1] - tlist[0]
processor.load_circuit(circuit, schedule_mode="ASAP")
tlist = processor.get_full_tlist()
time_scheduled1 = tlist[-1] - tlist[0]
processor.load_circuit(circuit, schedule_mode="ALAP")
tlist = processor.get_full_tlist()
time_scheduled2 = tlist[-1] - tlist[0]
assert (abs(time_scheduled1 * 2 - time_not_scheduled) < 1.0e-10)
assert (abs(time_scheduled2 * 2 - time_not_scheduled) < 1.0e-10)
def test_numerical_evolution(num_qubits, gates, device_class, kwargs):
num_qubits = 3
circuit = QubitCircuit(num_qubits)
for gate in gates:
circuit.add_gate(gate)
device = device_class(num_qubits, **kwargs)
device.load_circuit(circuit)
state = qutip.rand_ket(2**num_qubits)
state.dims = [[2] * num_qubits, [1] * num_qubits]
target = gate_sequence_product([state] + circuit.propagators())
if len(device.dims) > num_qubits:
num_ancilla = len(device.dims) - num_qubits
ancilla_indices = slice(0, num_ancilla)
extra = qutip.basis(device.dims[ancilla_indices], [0] * num_ancilla)
init_state = qutip.tensor(extra, state)
else:
init_state = state
options = qutip.Options(store_final_state=True, nsteps=50_000)
result = device.run_state(init_state=init_state,
analytical=False,
options=options)
if len(device.dims) > num_qubits:
target = qutip.tensor(extra, target)
assert _tol > abs(1 - qutip.metrics.fidelity(result.final_state, target))
def construct_circuit(self, angles):
"""
Construct a circuit by specifying values for each
free parameter.
Parameters
----------
angles: list of float
A list of dimension (n,) for n free parameters in the circuit
Returns
-------
circ: :obj:`.QubitCircuit`
"""
circ = QubitCircuit(self.num_qubits)
circ.user_gates = self.user_gates
i = 0
for layer_num in range(self.num_layers):
for block in self.blocks:
if block.initial and layer_num > 0:
continue
if block.is_native_gate:
circ.add_gate(block.operator, targets=block.targets)
else:
n = block.get_free_parameters_num()
circ.add_gate(
block.name,
targets=list(range(self.num_qubits)),
arg_value=angles[i:i + n] if n > 0 else None,
)
i += n
return circ
def test_compiler_result_format():
"""
Test if compiler return correctly different kind of compiler result
and if processor can successfully read them.
"""
num_qubits = 1
circuit = QubitCircuit(num_qubits)
circuit.add_gate("RX", targets=[0], arg_value=np.pi / 2)
processor = LinearSpinChain(num_qubits)
compiler = SpinChainCompiler(num_qubits,
params=processor.params,
setup="circular")
tlist, coeffs = compiler.compile(circuit)
assert (isinstance(tlist, dict))
assert ("sx0" in tlist)
assert (isinstance(coeffs, dict))
assert ("sx0" in coeffs)
processor.coeffs = coeffs
processor.set_all_tlist(tlist)
assert_array_equal(processor.pulses[0].coeff, coeffs["sx0"])
assert_array_equal(processor.pulses[0].tlist, tlist["sx0"])
compiler.gate_compiler["RX"] = rx_compiler_without_pulse_dict
tlist, coeffs = compiler.compile(circuit)
assert (isinstance(tlist, dict))
assert (0 in tlist)
assert (isinstance(coeffs, dict))
assert (0 in coeffs)
processor.coeffs = coeffs
processor.set_all_tlist(tlist)
assert_array_equal(processor.pulses[0].coeff, coeffs[0])
assert_array_equal(processor.pulses[0].tlist, tlist[0])
def test_simple_hadamard(self):
"""
Test for optimizing a simple hadamard gate
"""
N = 1
H_d = sigmaz()
H_c = sigmax()
qc = QubitCircuit(N)
qc.add_gate("SNOT", 0)
# test load_circuit, with verbose info
num_tslots = 10
evo_time = 10
test = OptPulseProcessor(N, drift=H_d)
test.add_control(H_c, targets=0)
tlist, coeffs = test.load_circuit(qc,
num_tslots=num_tslots,
evo_time=evo_time,
verbose=True)
# test run_state
rho0 = qubit_states(1, [0])
plus = (qubit_states(1, [0]) + qubit_states(1, [1])).unit()
result = test.run_state(rho0)
assert_allclose(fidelity(result.states[-1], plus), 1, rtol=1.0e-6)
# test add/remove ctrl
test.add_control(sigmay())
test.remove_pulse(0)
assert_(len(test.pulses) == 1,
msg="Method of remove_pulse could be wrong.")
assert_allclose(test.drift.drift_hamiltonians[0].qobj, H_d)
assert_(sigmay() in test.ctrls,
msg="Method of remove_pulse could be wrong.")
def test_user_gate(self):
"""
User defined gate for QubitCircuit
"""
def customer_gate1(arg_values):
mat = np.zeros((4, 4), dtype=np.complex128)
mat[0, 0] = mat[1, 1] = 1.
mat[2:4, 2:4] = gates.rx(arg_values).full()
return Qobj(mat, dims=[[2, 2], [2, 2]])
def customer_gate2():
mat = np.array([[1., 0],
[0., 1.j]])
return Qobj(mat, dims=[[2], [2]])
qc = QubitCircuit(3)
qc.user_gates = {"CTRLRX": customer_gate1,
"T1": customer_gate2}
qc.add_gate("CTRLRX", targets=[1, 2], arg_value=np.pi/2)
qc.add_gate("T1", targets=[1])
props = qc.propagators()
result1 = tensor(identity(2), customer_gate1(np.pi/2))
np.testing.assert_allclose(props[0].full(), result1.full())
result2 = tensor(identity(2), customer_gate2(), identity(2))
np.testing.assert_allclose(props[1].full(), result2.full())
def test_qasm_str():
expected_qasm_str = ('// QASM 2.0 file generated by QuTiP\n\nOPENQASM 2.0;'
'\ninclude "qelib1.inc";\n\nqreg q[2];\ncreg c[1];\n\n'
'x q[0];\nmeasure q[1] -> c[0]\n')
simple_qc = QubitCircuit(2, num_cbits=1)
simple_qc.add_gate("X", targets=[0])
simple_qc.add_measurement("M", targets=[1], classical_store=0)
assert circuit_to_qasm_str(simple_qc) == expected_qasm_str
def test_device_against_gate_sequence(num_qubits, gates, device_class, kwargs):
circuit = QubitCircuit(num_qubits)
for gate in gates:
circuit.add_gate(gate)
U_ideal = gate_sequence_product(circuit.propagators())
device = device_class(num_qubits)
U_physical = gate_sequence_product(device.run(circuit))
assert (U_ideal - U_physical).norm() < _tol
def test_scqubits_single_qubit_gate():
# Check the accuracy of the single-qubit gate for SCQubits.
circuit = QubitCircuit(1)
circuit.add_gate("X", targets=[0])
processor = SCQubits(1, omega_single=0.04)
processor.load_circuit(circuit)
U = _compute_propagator(processor, circuit)
fid = qutip.average_gate_fidelity(qutip.Qobj(U.full()[:2, :2]),
qutip.sigmax())
assert pytest.approx(fid, rel=1.0e-6) == 1
def test_zz_cross_talk(self):
circuit = QubitCircuit(2)
circuit.add_gate("X", 0)
processor = SCQubits(2)
processor.add_noise(ZZCrossTalk(processor.params))
processor.load_circuit(circuit)
pulses = processor.get_noisy_pulses(device_noise=True, drift=True)
for pulse in pulses:
if not isinstance(pulse, Drift) and pulse.label=="systematic_noise":
assert(len(pulse.coherent_noise) == 1)
def _circuit1():
circuit1 = QubitCircuit(6)
circuit1.add_gate("SNOT", 2)
circuit1.add_gate("CNOT", 4, 2)
circuit1.add_gate("CNOT", 3, 2)
circuit1.add_gate("CNOT", 1, 2)
circuit1.add_gate("CNOT", 5, 4)
circuit1.add_gate("CNOT", 1, 5)
circuit1.add_gate("SWAP", [0, 1])
return circuit1
def testFREDKINdecompose(self):
"""
FREDKIN to rotation and CNOT: compare unitary matrix for FREDKIN and product of
resolved matrices in terms of rotation gates and CNOT.
"""
qc1 = QubitCircuit(3)
qc1.add_gate("FREDKIN", targets=[0, 1], controls=[2])
U1 = gates.gate_sequence_product(qc1.propagators())
qc2 = qc1.resolve_gates()
U2 = gates.gate_sequence_product(qc2.propagators())
assert _op_dist(U1, U2) < 1e-12
def testSNOTdecompose(self):
"""
SNOT to rotation: compare unitary matrix for SNOT and product of
resolved matrices in terms of rotation gates.
"""
qc1 = QubitCircuit(1)
qc1.add_gate("SNOT", targets=0)
U1 = gates.gate_sequence_product(qc1.propagators())
qc2 = qc1.resolve_gates()
U2 = gates.gate_sequence_product(qc2.propagators())
assert _op_dist(U1, U2) < 1e-12
def test_analytical_evolution(num_qubits, gates, device_class, kwargs):
circuit = QubitCircuit(num_qubits)
for gate in gates:
circuit.add_gate(gate)
state = qutip.rand_ket(2**num_qubits)
state.dims = [[2] * num_qubits, [1] * num_qubits]
ideal = gate_sequence_product([state] + circuit.propagators())
device = device_class(num_qubits)
operators = device.run_state(init_state=state, qc=circuit, analytical=True)
result = gate_sequence_product(operators)
assert abs(qutip.metrics.fidelity(result, ideal) - 1) < _tol
def testadjacentgates(self):
"""
Adjacent Gates: compare unitary matrix for ISWAP and product of
resolved matrices in terms of adjacent gates interaction.
"""
qc1 = QubitCircuit(3)
qc1.add_gate("ISWAP", targets=[0, 2])
U1 = gates.gate_sequence_product(qc1.propagators())
qc0 = qc1.adjacent_gates()
qc2 = qc0.resolve_gates(basis="ISWAP")
U2 = gates.gate_sequence_product(qc2.propagators())
assert _op_dist(U1, U2) < 1e-12
def _teleportation_circuit2():
teleportation = QubitCircuit(3, num_cbits=2,
input_states=["q0", "0", "0", "c0", "c1"])
teleportation.add_gate("SNOT", targets=[1])
teleportation.add_gate("CNOT", targets=[2], controls=[1])
teleportation.add_gate("CNOT", targets=[1], controls=[0])
teleportation.add_gate("SNOT", targets=[0])
teleportation.add_gate("CNOT", targets=[2], controls=[1])
teleportation.add_gate("CZ", targets=[2], controls=[0])
return teleportation
def test_collapse_with_different_tlist(self):
"""
Test if there are errors raised because of wrong tlist handling.
"""
qc = QubitCircuit(1)
qc.add_gate("X", 0)
proc = LinearSpinChain(1)
proc.load_circuit(qc)
tlist = np.linspace(0, 30., 100)
coeff = tlist * 0.1
noise = DecoherenceNoise(sigmam(), targets=0, coeff=coeff, tlist=tlist)
proc.add_noise(noise)
proc.run_state(basis(2, 0))
def test_pulse_plotting(processor_class):
try:
import matplotlib.pyplot as plt
except Exception:
return True
qc = QubitCircuit(3)
qc.add_gate("CNOT", 1, 0)
qc.add_gate("X", 1)
processor = processor_class(3)
processor.load_circuit(qc)
fig, ax = processor.plot_pulses()
plt.close(fig)
def test_globalphase_gate_propagators(self):
qc = QubitCircuit(2)
qc.add_gate("GLOBALPHASE", arg_value=np.pi / 2)
[gate] = qc.gates
assert gate.name == "GLOBALPHASE"
assert gate.arg_value == np.pi / 2
[U_expanded] = qc.propagators()
assert U_expanded == 1j * qp.qeye([2, 2])
[U_unexpanded] = qc.propagators(expand=False)
assert U_unexpanded == 1j * qp.qeye([2, 2])
def test_add_circuit(self):
"""
Addition of a circuit to a `QubitCircuit`
"""
qc = QubitCircuit(6)
qc.add_gate("CNOT", targets=[1], controls=[0])
test_gate = Gate("SWAP", targets=[1, 4])
qc.add_gate(test_gate)
qc.add_gate("TOFFOLI", controls=[0, 1], targets=[2])
qc.add_gate("SNOT", targets=[3])
qc.add_gate(test_gate, index=[3])
qc.add_measurement("M0", targets=[0], classical_store=[1])
qc.add_1q_gate("RY", start=4, end=5, arg_value=1.570796)
qc1 = QubitCircuit(6)
qc1.add_circuit(qc)
# Test if all gates and measurements are added
assert len(qc1.gates) == len(qc.gates)
for i in range(len(qc1.gates)):
assert (qc1.gates[i].name == qc.gates[i].name)
assert (qc1.gates[i].targets == qc.gates[i].targets)
if (isinstance(qc1.gates[i], Gate)
and isinstance(qc.gates[i], Gate)):
assert (qc1.gates[i].controls == qc.gates[i].controls)
assert (qc1.gates[i].classical_controls ==
qc.gates[i].classical_controls)
elif (isinstance(qc1.gates[i], Measurement)
and isinstance(qc.gates[i], Measurement)):
assert (qc1.gates[i].classical_store ==
qc.gates[i].classical_store)
# Test exception when qubit out of range
pytest.raises(NotImplementedError, qc1.add_circuit, qc, start=4)
qc2 = QubitCircuit(8)
qc2.add_circuit(qc, start=2)
# Test if all gates are added
assert len(qc2.gates) == len(qc.gates)
# Test if the positions are correct
for i in range(len(qc2.gates)):
if qc.gates[i].targets is not None:
assert (qc2.gates[i].targets[0] == qc.gates[i].targets[0] + 2)
if (isinstance(qc.gates[i], Gate)
and qc.gates[i].controls is not None):
assert (qc2.gates[i].controls[0] == qc.gates[i].controls[0] +
2)
def test_compiler_with_continous_pulse(spline_kind, schedule_mode):
num_qubits = 2
circuit = QubitCircuit(num_qubits)
circuit.add_gate("X", targets=0)
circuit.add_gate("X", targets=1)
circuit.add_gate("X", targets=0)
processor = CircularSpinChain(num_qubits)
gauss_compiler = MyCompiler(
processor.N, processor.params, processor.pulse_dict)
processor.load_circuit(
circuit, schedule_mode = schedule_mode, compiler=gauss_compiler)
result = processor.run_state(init_state = basis([2,2], [0,0]))
assert(abs(fidelity(result.states[-1],basis([2,2],[0,1])) - 1) < 1.e-6)
def test_with_model(self):
model = SpinChainModel(3, setup="linear")
processor = OptPulseProcessor(3, model=model)
qc = QubitCircuit(3)
qc.add_gate("CNOT", 1, 0)
qc.add_gate("X", 2)
processor.load_circuit(qc,
merge_gates=True,
num_tslots=10,
evo_time=2.0)
init_state = qutip.rand_ket(8, dims=[[2, 2, 2], [1, 1, 1]])
num_result = processor.run_state(init_state=init_state).states[-1]
ideal_result = qc.run(init_state)
assert (pytest.approx(qutip.fidelity(num_result, ideal_result),
1.0e-5) == 1.0)
def test_compiler_without_pulse_dict():
"""
Test for a compiler function without pulse_dict and using args.
"""
num_qubits = 2
circuit = QubitCircuit(num_qubits)
circuit.add_gate("X", targets=[0])
circuit.add_gate("X", targets=[1])
processor = CircularSpinChain(num_qubits)
compiler = SpinChainCompiler(
num_qubits, params=processor.params, pulse_dict=None, setup="circular")
compiler.gate_compiler["RX"] = rx_compiler_without_pulse_dict
compiler.args = {"params": processor.params}
processor.load_circuit(circuit, compiler=compiler)
result = processor.run_state(basis([2,2], [0,0]))
assert(abs(fidelity(result.states[-1], basis([2,2], [1,1])) - 1.) < 1.e-6 )
def test_export_image(self, in_temporary_directory):
from qutip_qip import circuit_latex
qc = QubitCircuit(2, reverse_states=False)
qc.add_gate("CSIGN", controls=[0], targets=[1])
if "png" in circuit_latex.CONVERTERS:
file_png200 = "exported_pic_200.png"
file_png400 = "exported_pic_400.png"
qc.draw("png", 200, file_png200.split('.')[0], "")
qc.draw("png", 400.5, file_png400.split('.')[0], "")
assert file_png200 in os.listdir('.')
assert file_png400 in os.listdir('.')
assert os.stat(file_png200).st_size < os.stat(file_png400).st_size
if "svg" in circuit_latex.CONVERTERS:
file_svg = "exported_pic.svg"
qc.draw("svg", file_svg.split('.')[0], "")
assert file_svg in os.listdir('.')
def single_crosstalk_simulation(num_gates):
""" A single simulation, with num_gates representing the number of rotations.
Args:
num_gates (int): The number of random gates to add in the simulation.
Returns:
result (qutip.solver.Result): A qutip Result object obtained from any of the
solver methods such as mesolve.
"""
num_qubits = 2 # Qubit-0 is the target qubit. Qubit-1 suffers from crosstalk.
myprocessor = ModelProcessor(model=MyModel(num_qubits))
# Add qubit frequency detuning 1.852MHz for the second qubit.
myprocessor.add_drift(2 * np.pi * (sigmaz() + 1) / 2 * 1.852, targets=1)
myprocessor.native_gates = None # Remove the native gates
mycompiler = MyCompiler(num_qubits, {
"pulse_amplitude": 0.02,
"duration": 25
})
myprocessor.add_noise(ClassicalCrossTalk(1.0))
# Define a randome circuit.
gates_set = [
Gate("ROT", 0, arg_value=0),
Gate("ROT", 0, arg_value=np.pi / 2),
Gate("ROT", 0, arg_value=np.pi),
Gate("ROT", 0, arg_value=np.pi / 2 * 3),
]
circuit = QubitCircuit(num_qubits)
for ind in np.random.randint(0, 4, num_gates):
circuit.add_gate(gates_set[ind])
# Simulate the circuit.
myprocessor.load_circuit(circuit, compiler=mycompiler)
init_state = tensor(
[Qobj([[init_fid, 0], [0, 0.025]]),
Qobj([[init_fid, 0], [0, 0.025]])])
options = SolverOptions(nsteps=10000) # increase the maximal allowed steps
e_ops = [tensor([qeye(2), fock_dm(2)])] # observable
# compute results of the run using a solver of choice with custom options
result = myprocessor.run_state(init_state,
solver="mesolve",
options=options,
e_ops=e_ops)
result = result.expect[0][-1] # measured expectation value at the end
return result
