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 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_gate_product(self):
filename = "qft.qasm"
filepath = Path(__file__).parent / 'qasm_files' / filename
qc = read_qasm(filepath)
U_list_expanded = qc.propagators()
U_list = qc.propagators(expand=False)
inds_list = []
for gate in qc.gates:
if isinstance(gate, Measurement):
continue
else:
inds_list.append(gate.get_inds(qc.N))
U_1, _ = gate_sequence_product(U_list,
inds_list=inds_list,
expand=True)
U_2 = gate_sequence_product(U_list_expanded,
left_to_right=True,
expand=False)
np.testing.assert_allclose(U_1, U_2)
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_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 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 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 test_numerical_circuit(circuit, device_class, kwargs, schedule_mode):
num_qubits = circuit.N
with warnings.catch_warnings(record=True):
device = device_class(circuit.N, **kwargs)
device.load_circuit(circuit, schedule_mode=schedule_mode)
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)
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(result.final_state, target))
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_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 testQFTComparison(self):
"""
qft: compare qft and product of qft steps
"""
for N in range(1, 5):
U1 = qft(N)
U2 = gate_sequence_product(qft_steps(N))
assert_((U1 - U2).norm() < 1e-12)
def compute_jac(self, angles, indices_to_compute=None):
"""
Compute the jacobian for the circuit's cost function,
assuming the cost function is in observable mode.
Parameters
----------
angles: list of float
Circuit free parameters
indicies_to_compute: list of int, optional
Block indices for which to use in computing the jacobian.
By default, this is every index (every block).
Returns
-------
jac: (n,) numpy array of floats
"""
if indices_to_compute is None:
indices_to_compute = list(range(len(angles)))
circ = self.construct_circuit(angles)
propagators = circ.propagators()
U = gate_sequence_product(propagators)
U_prods, U_prods_back = self.get_unitary_products(propagators)
# subtract one for the identity matrix
n = len(U_prods) - 1
def modify_unitary(k, U):
return U_prods_back[n - 1 - k] * U * U_prods[k]
jacobian = []
i = 0
for k, block in enumerate(self.get_block_series()):
n_params = block.get_free_parameters_num()
if n_params > 0:
if i in indices_to_compute:
dBlock = block.get_unitary_derivative(angles[i:i +
n_params])
dU = modify_unitary(k, dBlock)
jacobian.append(self.cost_derivative(U, dU))
i += n_params
return np.array(jacobian)
def test_multi_gates(self):
N = 2
H_d = tensor([sigmaz()] * 2)
H_c = []
test = OptPulseProcessor(N)
test.add_drift(H_d, [0, 1])
test.add_control(sigmax(), cyclic_permutation=True)
test.add_control(sigmay(), cyclic_permutation=True)
test.add_control(tensor([sigmay(), sigmay()]))
# qubits circuit with 3 gates
setting_args = {
"SNOT": {
"num_tslots": 10,
"evo_time": 1
},
"SWAP": {
"num_tslots": 30,
"evo_time": 3
},
"CNOT": {
"num_tslots": 30,
"evo_time": 3
}
}
qc = QubitCircuit(N)
qc.add_gate("SNOT", 0)
qc.add_gate("SWAP", targets=[0, 1])
qc.add_gate('CNOT', controls=1, targets=[0])
test.load_circuit(qc, setting_args=setting_args, merge_gates=False)
rho0 = rand_ket(4) # use random generated ket state
rho0.dims = [[2, 2], [1, 1]]
U = gate_sequence_product(qc.propagators())
rho1 = U * rho0
result = test.run_state(rho0)
assert_(fidelity(result.states[-1], rho1) > 1 - 1.0e-6)
