def cy_grape_unitary(
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=True,
progress_bar=BaseProgressBar(),
):
"""
Calculate control pulses for the Hamitonian 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])
M = len(times)
J = len(H_ops)
u = np.zeros((R, J, M))
H_ops_data = [H_op.data for H_op in H_ops]
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_limits:
use_u_limits = 1
u_min = u_limits[0]
u_max = u_limits[1]
else:
use_u_limits = 0
u_min = 0.0
u_max = 0.0
if u_start is not None:
for idx, u0 in enumerate(u_start):
u[0, idx, :] = u0
if beta:
warnings.warn("Causion: Using experimental feature time-penalty")
alpha_val = alpha if alpha else 0.0
beta_val = beta if beta else 0.0
progress_bar.start(R)
for r in range(R - 1):
progress_bar.update(r)
dt = times[1] - times[0]
if use_interp:
ip_funcs = [
interp1d(times, u[r, j, :], kind=interp_kind, bounds_error=False, fill_value=u[r, j, -1])
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().data for idx in range(M - 1)]
else:
def _H_idx(idx):
return H0 + sum([u[r, j, idx] * H_ops[j] for j in range(J)])
U_list = [(-1j * _H_idx(idx) * dt).expm().data for idx in range(M - 1)]
U_f_list = []
U_b_list = []
U_f = 1
U_b = sp.eye(*(U.shape))
for n in range(M - 1):
U_f = U_list[n] * U_f
U_f_list.append(U_f)
U_b_list.insert(0, U_b)
U_b = U_list[M - 2 - n].T.conj().tocsr() * U_b
cy_grape_inner(
U.data,
u,
r,
J,
M,
U_b_list,
U_f_list,
H_ops_data,
dt,
eps,
alpha_val,
beta_val,
phase_sensitive,
use_u_limits,
u_min,
u_max,
)
if use_interp:
ip_funcs = [
interp1d(times, u[R - 1, j, :], 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[-1, j, :]] for j in range(J)]
progress_bar.finished()
return GRAPEResult(u=u, U_f=Qobj(U_f_list[-1], dims=U.dims), H_t=H_td_func)
def cy_grape_unitary(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=True,
progress_bar=BaseProgressBar()):
"""
Calculate control pulses for the Hamitonian 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])
M = len(times)
J = len(H_ops)
u = np.zeros((R, J, M))
H_ops_data = [H_op.data for H_op in H_ops]
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_limits:
use_u_limits = 1
u_min = u_limits[0]
u_max = u_limits[1]
else:
use_u_limits = 0
u_min = 0.0
u_max = 0.0
if u_start is not None:
for idx, u0 in enumerate(u_start):
u[0, idx, :] = u0
if beta:
warnings.warn("Causion: Using experimental feature time-penalty")
alpha_val = alpha if alpha else 0.0
beta_val = beta if beta else 0.0
progress_bar.start(R)
for r in range(R - 1):
progress_bar.update(r)
dt = times[1] - times[0]
if use_interp:
ip_funcs = [
interp1d(times,
u[r, j, :],
kind=interp_kind,
bounds_error=False,
fill_value=u[r, j, -1]) 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().data
for idx in range(M - 1)]
else:
def _H_idx(idx):
return H0 + sum([u[r, j, idx] * H_ops[j] for j in range(J)])
U_list = [(-1j * _H_idx(idx) * dt).expm().data
for idx in range(M - 1)]
U_f_list = []
U_b_list = []
U_f = 1
U_b = sp.eye(*(U.shape))
for n in range(M - 1):
U_f = U_list[n] * U_f
U_f_list.append(U_f)
U_b_list.insert(0, U_b)
U_b = U_list[M - 2 - n].T.conj().tocsr() * U_b
cy_grape_inner(U.data, u, r, J, M, U_b_list, U_f_list, H_ops_data, dt,
eps, alpha_val, beta_val, phase_sensitive, use_u_limits,
u_min, u_max)
if use_interp:
ip_funcs = [
interp1d(times,
u[R - 1, j, :],
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[-1, j, :]] for j in range(J)]
progress_bar.finished()
return GRAPEResult(u=u, U_f=Qobj(U_f_list[-1], dims=U.dims), H_t=H_td_func)