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_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_device_against_gate_sequence(num_qubits, gates, device_class, kwargs):
circuit = qutip.qip.circuit.QubitCircuit(num_qubits)
for gate in gates:
circuit.add_gate(gate)
U_ideal = gate_sequence_product(circuit.propagators())
device = device_class(num_qubits, correct_global_phase=True)
U_physical = gate_sequence_product(device.run(circuit))
assert (U_ideal - U_physical).norm() < _tol
def test_device_against_gate_sequence(gates):
n_qubits = 3
circuit = qutip.qip.circuit.QubitCircuit(n_qubits)
for gate in gates:
circuit.add_gate(gate)
U_ideal = gate_sequence_product(circuit.propagators())
device = DispersiveCavityQED(n_qubits, correct_global_phase=True)
U_physical = gate_sequence_product(device.run(circuit))
assert (U_ideal - U_physical).norm() < _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 test_analytical_evolution(num_qubits, gates, device_class, kwargs):
circuit = qutip.qip.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, correct_global_phase=True)
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 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 testISWAPtoCNOT(self):
"""
ISWAP to CNOT: compare unitary matrix for ISWAP and product of
resolved matrices in terms of CNOT.
"""
qc1 = QubitCircuit(2)
qc1.add_gate("ISWAP", targets=[0, 1])
U1 = gate_sequence_product(qc1.propagators())
qc2 = qc1.resolve_gates(basis="CNOT")
U2 = gate_sequence_product(qc2.propagators())
assert_((U1 - U2).norm() < 1e-12)
def testCNOTtoCSIGN(self):
"""
CNOT to CSIGN: compare unitary matrix for CNOT and product of
resolved matrices in terms of CSIGN.
"""
qc1 = QubitCircuit(2)
qc1.add_gate("CNOT", targets=[0], controls=[1])
U1 = gate_sequence_product(qc1.propagators())
qc2 = qc1.resolve_gates(basis="CSIGN")
U2 = gate_sequence_product(qc2.propagators())
assert_((U1 - U2).norm() < 1e-12)
def test_analytical_evolution():
n_qubits = 3
circuit = qutip.qip.circuit.QubitCircuit(n_qubits)
for gate in [_iswap, _rz, _rx]:
circuit.add_gate(gate)
state = qutip.rand_ket(2**n_qubits)
state.dims = [[2] * n_qubits, [1] * n_qubits]
ideal = gate_sequence_product([state] + circuit.propagators())
device = DispersiveCavityQED(n_qubits, correct_global_phase=True)
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 test_numerical_evolution(num_qubits, gates, device_class, kwargs):
num_qubits = 3
circuit = qutip.qip.circuit.QubitCircuit(num_qubits)
for gate in gates:
circuit.add_gate(gate)
with warnings.catch_warnings(record=True):
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 test_linear_SQRTISWAP(self):
"""
Linear Spin Chain Setup: compare unitary matrix for SQRTISWAP and
propogator matrix of the implemented physical model.
"""
N = 3
qc = QubitCircuit(N)
qc.add_gate("SQRTISWAP", targets=[0, 1])
U_ideal = gate_sequence_product(qc.propagators())
p = LinearSpinChain(N, correct_global_phase=True)
U_list = p.run(qc)
U_physical = gate_sequence_product(U_list)
assert_((U_ideal - U_physical).norm() < 1e-12)
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 test_numerical_evo(self):
"""
Test run_state with qutip solver
"""
N = 3
qc = QubitCircuit(N)
qc.add_gate("RX", targets=[0], arg_value=np.pi / 2)
qc.add_gate("CNOT", targets=[0], controls=[1])
qc.add_gate("ISWAP", targets=[2, 1])
qc.add_gate("CNOT", targets=[0], controls=[2])
qc.add_gate("SQRTISWAP", targets=[0, 2])
qc.add_gate("RZ", arg_value=np.pi / 2, targets=[1])
# CircularSpinChain
test = CircularSpinChain(N)
tlist, coeffs = test.load_circuit(qc)
init_state = rand_ket(2**N)
init_state.dims = [[2] * N, [1] * N]
rho1 = gate_sequence_product([init_state] + qc.propagators())
result = test.run_state(
init_state=init_state,
analytical=False,
options=Options(store_final_state=True)).final_state
assert_allclose(
fidelity(result, rho1),
1.,
rtol=1e-6,
err_msg="Numerical run_state fails in CircularSpinChain")
# LinearSpinChain
test = LinearSpinChain(N)
tlist, coeffs = test.load_circuit(qc)
init_state = rand_ket(2**N)
init_state.dims = [[2] * N, [1] * N]
rho1 = gate_sequence_product([init_state] + qc.propagators())
result = test.run_state(
init_state=init_state,
analytical=False,
options=Options(store_final_state=True)).final_state
assert_allclose(fidelity(result, rho1),
1.,
rtol=1e-6,
err_msg="Numerical run_state fails in LinearSpinChain")
def test_linear_combination(self):
"""
Linear Spin Chain Setup: compare unitary matrix for ISWAP, SQRTISWAP,
RX and RY gates and the propogator matrix of the implemented physical
model.
"""
N = 3
qc = QubitCircuit(N)
qc.add_gate("ISWAP", targets=[0, 1])
qc.add_gate("SQRTISWAP", targets=[0, 1])
qc.add_gate("RZ", arg_value=np.pi / 2, arg_label=r"\pi/2", targets=[1])
qc.add_gate("RX", arg_value=np.pi / 2, arg_label=r"\pi/2", targets=[0])
U_ideal = gate_sequence_product(qc.propagators())
p = LinearSpinChain(N, correct_global_phase=True)
U_list = p.run(qc)
U_physical = gate_sequence_product(U_list)
assert_((U_ideal - U_physical).norm() < 1e-12)
def hst_measurement(state: Qobj, qcircuit, sample_size=1):
"""Hilbert-Schmidt test"""
N = len(state.dims[0])
qc = QubitCircuit(N * 2)
qc.add_circuit(qcircuit)
if state.isket:
statein = tensor(state, qubit_states(N))
elif state.isbra:
statein = tensor(state, qubit_states(N).dag())
elif state.isoper:
statein = tensor(state, ket2dm(qubit_states(N)))
state_preps = statein.transform(
gate_sequence_product(bell_prep(N, True).propagators()))
state_out = state_preps.transform(gate_sequence_product(qc.propagators()))
state_postps = state_out.transform(
gate_sequence_product(bell_prep(N).propagators()))
prob = com_measure(state_postps)
hst = np.random.choice(4**N, sample_size, p=prob)
mresult = [
state_index_number(state_postps.dims[0], result) for result in hst
]
return mresult # raw output
def dst_measurement(state1, state2, sample_size=1):
"""destructive swap test"""
N = len(state1.dims[0])
if len(state2.dims[0]) != N:
raise ValueError("Dimensions of states dismatch.")
statein = tensor(state1, state2)
stateout = statein.transform(
gate_sequence_product(bell_prep(N, True).propagators()))
prob = com_measure(stateout)
result_in_number = np.random.choice(4**N, sample_size, p=prob)
mresult = [
state_index_number(stateout.dims[0], result)
for result in result_in_number
]
return mresult # raw output
def test_numerical_evolution():
n_qubits = 3
circuit = qutip.qip.circuit.QubitCircuit(n_qubits)
circuit.add_gate("RX", targets=[0], arg_value=np.pi / 2)
circuit.add_gate("CNOT", targets=[0], controls=[1])
circuit.add_gate("ISWAP", targets=[2, 1])
circuit.add_gate("CNOT", targets=[0], controls=[2])
with warnings.catch_warnings(record=True):
device = DispersiveCavityQED(n_qubits, g=0.1)
device.load_circuit(circuit)
state = qutip.rand_ket(2**n_qubits)
state.dims = [[2] * n_qubits, [1] * n_qubits]
target = gate_sequence_product([state] + circuit.propagators())
extra = qutip.basis(10, 0)
options = qutip.Options(store_final_state=True, nsteps=50_000)
result = device.run_state(init_state=qutip.tensor(extra, state),
analytical=False,
options=options)
assert _tol > abs(1 - qutip.metrics.fidelity(result.final_state,
qutip.tensor(extra, target)))
def test_numerical_circuit(circuit, device_class, kwargs, schedule_mode):
num_qubits = circuit.N
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 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 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)
def grape_unitary_adaptive(U, H0, H_ops, R, times, eps=None, u_start=None,
u_limits=None, interp_kind='linear',
use_interp=False, alpha=None, beta=None,
phase_sensitive=False, overlap_terminate=1.0,
progress_bar=BaseProgressBar()):
"""
Calculate control pulses for the Hamiltonian operators in H_ops so that
the unitary U is realized.
Experimental: Work in progress.
Parameters
----------
U : Qobj
Target unitary evolution operator.
H0 : Qobj
Static Hamiltonian (that cannot be tuned by the control fields).
H_ops: list of Qobj
A list of operators that can be tuned in the Hamiltonian via the
control fields.
R : int
Number of GRAPE iterations.
time : array / list
Array of time coordinates for control pulse evalutation.
u_start : array
Optional array with initial control pulse values.
Returns
-------
Instance of GRAPEResult, which contains the control pulses calculated
with GRAPE, a time-dependent Hamiltonian that is defined by the
control pulses, as well as the resulting propagator.
"""
if eps is None:
eps = 0.1 * (2 * np.pi) / (times[-1])
eps_vec = np.array([eps / 2, eps, 2 * eps])
eps_log = np.zeros(R)
overlap_log = np.zeros(R)
best_k = 0
_k_overlap = np.array([0.0, 0.0, 0.0])
M = len(times)
J = len(H_ops)
K = len(eps_vec)
Uf = [None for _ in range(K)]
u = np.zeros((R, J, M, K))
if u_limits and len(u_limits) != 2:
raise ValueError("u_limits must be a list with two values")
if u_limits:
warnings.warn("Causion: Using experimental feature u_limits")
if u_limits and u_start:
# make sure that no values in u0 violates the u_limits conditions
u_start = np.array(u_start)
u_start[u_start < u_limits[0]] = u_limits[0]
u_start[u_start > u_limits[1]] = u_limits[1]
if u_start is not None:
for idx, u0 in enumerate(u_start):
for k in range(K):
u[0, idx, :, k] = u0
if beta:
warnings.warn("Causion: Using experimental feature time-penalty")
if phase_sensitive:
_fidelity_function = lambda x: x
else:
_fidelity_function = lambda x: abs(x) ** 2
best_k = 1
_r = 0
_prev_overlap = 0
progress_bar.start(R)
for r in range(R - 1):
progress_bar.update(r)
_r = r
eps_log[r] = eps_vec[best_k]
logger.debug("eps_vec: {}".format(eps_vec))
_t0 = time.time()
dt = times[1] - times[0]
if use_interp:
ip_funcs = [interp1d(times, u[r, j, :, best_k], kind=interp_kind,
bounds_error=False,
fill_value=u[r, j, -1, best_k])
for j in range(J)]
def _H_t(t, args=None):
return H0 + sum([float(ip_funcs[j](t)) * H_ops[j]
for j in range(J)])
U_list = [(-1j * _H_t(times[idx]) * dt).expm()
for idx in range(M-1)]
else:
def _H_idx(idx):
return H0 + sum([u[r, j, idx, best_k] * H_ops[j]
for j in range(J)])
U_list = [(-1j * _H_idx(idx) * dt).expm() for idx in range(M-1)]
logger.debug("Time 1: %fs" % (time.time() - _t0))
_t0 = time.time()
U_f_list = []
U_b_list = []
U_f = 1
U_b = 1
for m in range(M - 1):
U_f = U_list[m] * U_f
U_f_list.append(U_f)
U_b_list.insert(0, U_b)
U_b = U_list[M - 2 - m].dag() * U_b
logger.debug("Time 2: %fs" % (time.time() - _t0))
_t0 = time.time()
for j in range(J):
for m in range(M-1):
P = U_b_list[m] * U
Q = 1j * dt * H_ops[j] * U_f_list[m]
if phase_sensitive:
du = - cy_overlap(P.data, Q.data)
else:
du = (- 2 * cy_overlap(P.data, Q.data) *
cy_overlap(U_f_list[m].data, P.data))
if alpha:
# penalty term for high power control signals u
du += -2 * alpha * u[r, j, m, best_k] * dt
if beta:
# penalty term for late control signals u
du += -2 * beta * k ** 2 * u[r, j, k] * dt
for k, eps_val in enumerate(eps_vec):
u[r + 1, j, m, k] = u[r, j, m, k] + eps_val * du.real
if u_limits:
if u[r + 1, j, m, k] < u_limits[0]:
u[r + 1, j, m, k] = u_limits[0]
elif u[r + 1, j, m, k] > u_limits[1]:
u[r + 1, j, m, k] = u_limits[1]
u[r + 1, j, -1, :] = u[r + 1, j, -2, :]
logger.debug("Time 3: %fs" % (time.time() - _t0))
_t0 = time.time()
for k, eps_val in enumerate(eps_vec):
def _H_idx(idx):
return H0 + sum([u[r + 1, j, idx, k] * H_ops[j]
for j in range(J)])
U_list = [(-1j * _H_idx(idx) * dt).expm() for idx in range(M-1)]
Uf[k] = gate_sequence_product(U_list)
_k_overlap[k] = _fidelity_function(cy_overlap(Uf[k].data,
U.data)).real
best_k = np.argmax(_k_overlap)
logger.debug("k_overlap: ", _k_overlap, best_k)
if _prev_overlap > _k_overlap[best_k]:
logger.debug("Regression, stepping back with smaller eps.")
u[r + 1, :, :, :] = u[r, :, :, :]
eps_vec /= 2
else:
if best_k == 0:
eps_vec /= 2
elif best_k == 2:
eps_vec *= 2
_prev_overlap = _k_overlap[best_k]
overlap_log[r] = _k_overlap[best_k]
if overlap_terminate < 1.0:
if _k_overlap[best_k] > overlap_terminate:
logger.info("Reached target fidelity, terminating.")
break
logger.debug("Time 4: %fs" % (time.time() - _t0))
_t0 = time.time()
if use_interp:
ip_funcs = [interp1d(times, u[_r, j, :, best_k], kind=interp_kind,
bounds_error=False, fill_value=u[R - 1, j, -1])
for j in range(J)]
H_td_func = [H0] + [[H_ops[j], lambda t, args, j=j: ip_funcs[j](t)]
for j in range(J)]
else:
H_td_func = [H0] + [[H_ops[j], u[_r, j, :, best_k]] for j in range(J)]
progress_bar.finished()
result = GRAPEResult(u=u[:_r, :, :, best_k], U_f=Uf[best_k],
H_t=H_td_func)
result.eps = eps_log
result.overlap = overlap_log
return result
def load_circuit(self, qc, min_fid_err=np.inf, merge_gates=True,
setting_args=None, verbose=False, **kwargs):
"""
Find the pulses realizing a given :class:`qutip.qip.Circuit` using
`qutip.control.optimize_pulse_unitary`. Further parameter for
for `qutip.control.optimize_pulse_unitary` needs to be given as
keyword arguments. By default, it first merge all the gates
into one unitary and then find the control pulses for it.
It can be turned off and one can set different parameters
for different gates. See examples for details.
Examples
--------
# Same parameter for all the gates
qc = QubitCircuit(N=1)
qc.add_gate("SNOT", 0)
num_tslots = 10
evo_time = 10
processor = OptPulseProcessor(N=1, drift=sigmaz(), ctrls=[sigmax()])
# num_tslots and evo_time are two keyword arguments
tlist, coeffs = processor.load_circuit(
qc, num_tslots=num_tslots, evo_time=evo_time)
# Different parameters for different gates
qc = QubitCircuit(N=2)
qc.add_gate("SNOT", 0)
qc.add_gate("SWAP", targets=[0, 1])
qc.add_gate('CNOT', controls=1, targets=[0])
processor = OptPulseProcessor(N=2, drift=tensor([sigmaz()]*2))
processor.add_control(sigmax(), cyclic_permutation=True)
processor.add_control(sigmay(), cyclic_permutation=True)
processor.add_control(tensor([sigmay(), sigmay()]))
setting_args = {"SNOT": {"num_tslots": 10, "evo_time": 1},
"SWAP": {"num_tslots": 30, "evo_time": 3},
"CNOT": {"num_tslots": 30, "evo_time": 3}}
tlist, coeffs = processor.load_circuit(qc, setting_args=setting_args,
merge_gates=False)
Parameters
----------
qc: :class:`qutip.QubitCircuit` or list of Qobj
The quantum circuit to be translated.
min_fid_err: float, optional
The minimal fidelity tolerance, if the fidelity error of any
gate decomposition is higher, a warning will be given.
Default is infinite.
merge_gates: boolean, optimal
If True, merge all gate/Qobj into one Qobj and then
find the optimal pulses for this unitary matrix. If False,
find the optimal pulses for each gate/Qobj.
setting_args: dict, optional
Only considered if merge_gates is False.
It is a dictionary containing keyword arguments
for different gates.
E.g:
setting_args = {"SNOT": {"num_tslots": 10, "evo_time": 1},
"SWAP": {"num_tslots": 30, "evo_time": 3},
"CNOT": {"num_tslots": 30, "evo_time": 3}}
verbose: boolean, optional
If true, the information for each decomposed gate
will be shown. Default is False.
**kwargs
keyword arguments for `qutip.control.optimize_pulse_unitary`
Returns
-------
tlist: array_like
A NumPy array specifies the time of each coefficient
coeffs: array_like
A 2d NumPy array of the shape (len(ctrls), len(tlist)-1). Each
row corresponds to the control pulse sequence for
one Hamiltonian.
Notes
-----
len(tlist)-1=coeffs.shape[1] since tlist gives the beginning and the
end of the pulses
"""
if setting_args is None:
setting_args = {}
if isinstance(qc, QubitCircuit):
props = qc.propagators()
gates = [g.name for g in qc.gates]
elif isinstance(qc, Iterable):
props = qc
gates = None # using list of Qobj, no gates name
else:
raise ValueError(
"qc should be a "
"QubitCircuit or a list of Qobj")
if merge_gates: # merge all gates/Qobj into one Qobj
props = [gate_sequence_product(props)]
gates = None
time_record = [] # a list for all the gates
coeff_record = []
last_time = 0. # used in concatenation of tlist
for prop_ind, U_targ in enumerate(props):
U_0 = identity(U_targ.dims[0])
# If qc is a QubitCircuit and setting_args is not empty,
# we update the kwargs for each gate.
# keyword arguments in setting_arg have priority
if gates is not None and setting_args:
kwargs.update(setting_args[gates[prop_ind]])
full_drift_ham = self.drift.get_ideal_qobjevo(self.dims).cte
full_ctrls_hams = [pulse.get_ideal_qobj(self.dims)
for pulse in self.pulses]
result = cpo.optimize_pulse_unitary(
full_drift_ham, full_ctrls_hams, U_0, U_targ, **kwargs)
if result.fid_err > min_fid_err:
warnings.warn(
"The fidelity error of gate {} is higher "
"than required limit. Use verbose=True to see"
"the more detailed information.".format(prop_ind))
time_record.append(result.time[1:] + last_time)
last_time += result.time[-1]
coeff_record.append(result.final_amps.T)
if verbose:
print("********** Gate {} **********".format(prop_ind))
print("Final fidelity error {}".format(result.fid_err))
print("Final gradient normal {}".format(
result.grad_norm_final))
print("Terminated due to {}".format(result.termination_reason))
print("Number of iterations {}".format(result.num_iter))
tlist = np.hstack([[0.]] + time_record)
for i in range(len(self.pulses)):
self.pulses[i].tlist = tlist
coeffs = np.vstack([np.hstack(coeff_record)])
self.coeffs = coeffs
return tlist, coeffs
