def TestDrift():
"""
Test for Drift
"""
drift = Drift()
assert_allclose(drift.get_ideal_qobjevo(2).cte.norm(), 0)
drift.add_drift(sigmaz(), targets=1)
assert_allclose(
drift.get_ideal_qobjevo(dims=[3, 2]).cte, tensor(identity(3), sigmaz()))
class Processor(object):
"""
A simulator of a quantum device based on the QuTiP solver
:func:`qutip.mesolve`.
It is defined by the available driving Hamiltonian and
the decoherence time for each component systems.
The processor can simulate the evolution under the given
control pulses. Noisy evolution is supported by
:class:`qutip.qip.Noise` and can be added to the processor.
Parameters
----------
N: int
The number of component systems.
t1: list or float, optional
Characterize the decoherence of amplitude damping for
each qubit. A list of size `N` or a float for all qubits.
t2: list of float, optional
Characterize the decoherence of dephasing for
each qubit. A list of size `N` or a float for all qubits.
dims: list, optional
The dimension of each component system.
Default value is a
qubit system of ``dim=[2,2,2,...,2]``
spline_kind: str, optional
Type of the coefficient interpolation. Default is "step_func"
Note that they have different requirement for the length of `coeff'.
-"step_func":
The coefficient will be treated as a step function.
E.g. ``tlist=[0,1,2]`` and ``coeff=[3,2]``, means that the coefficient
is 3 in t=[0,1) and 2 in t=[2,3). It requires
``len(coeff)=len(tlist)-1`` or ``len(coeff)=len(tlist)``, but
in the second case the last element of `coeff` has no effect.
-"cubic": Use cubic interpolation for the coefficient. It requires
``len(coeff)=len(tlist)``
Attributes
----------
N: int
The number of component systems.
pulses: list of :class:`qutip.qip.Pulse`
A list of control pulses of this device
t1: float or list
Characterize the decoherence of amplitude damping of
each qubit.
t2: float or list
Characterize the decoherence of dephasing for
each qubit.
noise: :class:`qutip.qip.Noise`, optional
A list of noise objects. They will be processed when creating the
noisy :class:`qutip.QobjEvo` from the processor or run the simulation.
drift: :class:`qutip.qip.Drift`
A `Drift` object representing the drift Hamiltonians.
dims: list
The dimension of each component system.
Default value is a
qubit system of ``dim=[2,2,2,...,2]``
spline_kind: str
Type of the coefficient interpolation.
See parameters of :class:`qutip.qip.Processor` for details.
"""
def __init__(self,
N,
t1=None,
t2=None,
dims=None,
spline_kind="step_func"):
self.N = N
self.pulses = []
self.t1 = t1
self.t2 = t2
self.noise = []
self.drift = Drift()
if dims is None:
self.dims = [2] * N
else:
self.dims = dims
self.pulse_mode = "discrete"
self.spline_kind = spline_kind
@property
def num_qubits(self):
return self.N
@num_qubits.setter
def num_qubits(self, value):
self.N = value
def add_drift(self, qobj, targets, cyclic_permutation=False):
"""
Add a drift Hamiltonians. The drift Hamiltonians are intrinsic
of the quantum system and cannot be controlled by external field.
Parameters
----------
qobj: :class:`qutip.Qobj`
The drift Hamiltonian.
targets: list
The indices of the target qubits
(or subquantum system of other dimensions).
"""
if not isinstance(qobj, Qobj):
raise TypeError("The drift Hamiltonian must be a qutip.Qobj.")
if not qobj.isherm:
raise ValueError("The drift Hamiltonian must be Hermitian.")
num_qubits = len(qobj.dims[0])
if targets is None:
targets = list(range(num_qubits))
if not isinstance(targets, list):
targets = [targets]
if cyclic_permutation:
for i in range(self.N):
temp_targets = [(t + i) % self.N for t in targets]
self.drift.add_drift(qobj, temp_targets)
else:
self.drift.add_drift(qobj, targets)
def add_control(self,
qobj,
targets=None,
cyclic_permutation=False,
label=None):
"""
Add a control Hamiltonian to the processor. It creates a new
:class:`qutip.qip.Pulse`
object for the device that is turned off
(``tlist = None``, ``coeff = None``). To activate the pulse, one
can set its `tlist` and `coeff`.
Parameters
----------
qobj: :class:`qutip.Qobj`
The Hamiltonian for the control pulse..
targets: list, optional
The indices of the target qubits
(or subquantum system of other dimensions).
cyclic_permutation: bool, optional
If true, the Hamiltonian will be expanded for
all cyclic permutation of the target qubits.
label: str, optional
The label (name) of the pulse
"""
# Check validity of ctrl
if not isinstance(qobj, Qobj):
raise TypeError("The control Hamiltonian must be a qutip.Qobj.")
if not qobj.isherm:
raise ValueError("The control Hamiltonian must be Hermitian.")
num_qubits = len(qobj.dims[0])
if targets is None:
targets = list(range(num_qubits))
if not isinstance(targets, list):
targets = [targets]
if cyclic_permutation:
for i in range(self.N):
temp_targets = [(t + i) % self.N for t in targets]
if label is not None:
temp_label = label + "_" + str(temp_targets)
temp_label = label
self.pulses.append(
Pulse(qobj,
temp_targets,
spline_kind=self.spline_kind,
label=temp_label))
else:
self.pulses.append(
Pulse(qobj, targets, spline_kind=self.spline_kind,
label=label))
@property
def ctrls(self):
"""
A list of Hamiltonians of all pulses.
"""
result = []
for pulse in self.pulses:
result.append(pulse.get_ideal_qobj(self.dims))
return result
@property
def coeffs(self):
"""
A list of the coefficients for all control pulses.
"""
if not self.pulses:
return None
coeffs_list = [pulse.coeff for pulse in self.pulses]
return coeffs_list
@coeffs.setter
def coeffs(self, coeffs_list):
if len(coeffs_list) != len(self.pulses):
raise ValueError("The row number of coeffs must be same "
"as the number of control pulses.")
for i, coeff in enumerate(coeffs_list):
self.pulses[i].coeff = coeff
@property
def pulse_mode(self):
if self.spline_kind == "step_func":
return "discrete"
elif self.spline_kind == "cubic":
return "continuous"
else:
raise ValueError("Saved spline_kind not understood.")
@pulse_mode.setter
def pulse_mode(self, mode):
if mode == "discrete":
spline_kind = "step_func"
elif mode == "continuous":
spline_kind = "cubic"
else:
raise ValueError(
"Pulse mode must be either discrete or continuous.")
self.spline_kind = spline_kind
for pulse in self.pulses:
pulse.spline_kind = spline_kind
def get_full_tlist(self):
"""
Return the full tlist of the ideal pulses.
If different pulses have different time steps,
it will collect all the time steps in a sorted array.
Returns
-------
full_tlist: array-like 1d
The full time sequence for the ideal evolution.
"""
all_tlists = [
pulse.tlist for pulse in self.pulses if pulse.tlist is not None
]
if not all_tlists:
raise ValueError("No valid pulse found, tlist is empty.")
return np.unique(np.sort(np.hstack(all_tlists)))
def get_full_coeffs(self, full_tlist=None):
"""
Return the full coefficients in a 2d matrix form.
Each row corresponds to one pulse. If the `tlist` are
different for different pulses, the length of each row
will be same as the `full_tlist` (see method
`get_full_tlist`). Interpolation is used for
adding the missing coefficient according to `spline_kind`.
Returns
-------
coeffs: array-like 2d
The coefficients for all ideal pulses.
"""
# TODO add tests
self._is_pulses_valid()
if not self.pulses:
return np.array((0, 0), dtype=float)
if full_tlist is None:
full_tlist = self.get_full_tlist()
coeffs_list = []
for pulse in self.pulses:
if not isinstance(pulse.coeff, (bool, np.ndarray)):
raise ValueError("get_full_coeffs only works for "
"NumPy array or bool coeff.")
if isinstance(pulse.coeff, bool):
if pulse.coeff:
coeffs_list.append(np.ones(full_tlist))
else:
coeffs_list.append(np.zeros(full_tlist))
if self.spline_kind == "step_func":
arg = {"_step_func_coeff": True}
coeffs_list.append(
_fill_coeff(pulse.coeff, pulse.tlist, full_tlist, arg))
elif self.spline_kind == "cubic":
coeffs_list.append(
_fill_coeff(pulse.coeff, pulse.tlist, full_tlist, {}))
else:
raise ValueError("Unknown spline kind.")
return np.array(coeffs_list)
def set_all_tlist(self, tlist):
"""
Set the same `tlist` for all the pulses.
Parameters
----------
tlist: array-like, optional
A list of time at which the time-dependent coefficients are
applied. See :class:`qutip.qip.Pulse` for detailed information`
"""
for pulse in self.pulses:
pulse.tlist = tlist
def add_pulse(self, pulse):
"""
Add a new pulse to the device.
Parameters
----------
pulse: :class:`qutip.qip.Pulse`
`Pulse` object to be added.
"""
if isinstance(pulse, Pulse):
if pulse.spline_kind is None:
pulse.spline_kind = self.spline_kind
self.pulses.append(pulse)
else:
raise ValueError("Invalid input, pulse must be a Pulse object")
def remove_pulse(self, indices=None, label=None):
"""
Remove the control pulse with given indices.
Parameters
----------
indices: int or list of int
The indices of the control Hamiltonians to be removed.
label: str
The label of the pulse
"""
if indices is not None:
if not isinstance(indices, Iterable):
indices = [indices]
indices.sort(reverse=True)
for ind in indices:
del self.pulses[ind]
else:
for ind, pulse in enumerate(self.pulses):
if pulse.label == label:
del self.pulses[ind]
def _is_pulses_valid(self):
"""
Check if the pulses are in the correct shape.
Returns: bool
If they are valid or not
"""
for i, pulse in enumerate(self.pulses):
if pulse.coeff is None or isinstance(pulse.coeff, bool):
# constant pulse
continue
if pulse.tlist is None:
raise ValueError(
"Pulse id={} is invalid. "
"Please define a tlist for the pulse.".format(i))
if pulse.tlist is not None and pulse.coeff is None:
raise ValueError(
"Pulse id={} is invalid. "
"Please define a coeff for the pulse.".format(i))
coeff_len = len(pulse.coeff)
tlist_len = len(pulse.tlist)
if pulse.spline_kind == "step_func":
if coeff_len == tlist_len - 1 or coeff_len == tlist_len:
pass
else:
raise ValueError(
"The length of tlist and coeff of the pulse "
"labelled {} is invalid. "
"It's either len(tlist)=len(coeff) or "
"len(tlist)-1=len(coeff) for coefficients "
"as step function".format(i))
else:
if coeff_len == tlist_len:
pass
else:
raise ValueError(
"The length of tlist and coeff of the pulse "
"labelled {} is invalid. "
"It should be either len(tlist)=len(coeff)".format(i))
return True
def add_noise(self, noise):
"""
Add a noise object to the processor
Parameters
----------
noise: :class:`qutip.qip.Noise`
The noise object defined outside the processor
"""
if isinstance(noise, Noise):
self.noise.append(noise)
else:
raise TypeError("Input is not a Noise object.")
def save_coeff(self, file_name, inctime=True):
"""
Save a file with the control amplitudes in each timeslot.
Parameters
----------
file_name: string
Name of the file.
inctime: bool, optional
True if the time list should be included in the first column.
"""
self._is_pulses_valid()
coeffs = np.array(self.get_full_coeffs())
if inctime:
shp = coeffs.T.shape
data = np.empty((shp[0], shp[1] + 1), dtype=np.float64)
data[:, 0] = self.get_full_tlist()
data[:, 1:] = coeffs.T
else:
data = coeffs.T
np.savetxt(file_name, data, delimiter='\t', fmt='%1.16f')
def read_coeff(self, file_name, inctime=True):
"""
Read the control amplitudes matrix and time list
saved in the file by `save_amp`.
Parameters
----------
file_name: string
Name of the file.
inctime: bool, optional
True if the time list in included in the first column.
Returns
-------
tlist: array_like
The time list read from the file.
coeffs: array_like
The pulse matrix read from the file.
"""
data = np.loadtxt(file_name, delimiter='\t')
if not inctime:
self.coeffs = data.T
return self.coeffs
else:
tlist = data[:, 0]
self.set_all_tlist(tlist)
self.coeffs = data[:, 1:].T
return self.get_full_tlist, self.coeffs
def get_noisy_pulses(self, device_noise=False, drift=False):
"""
It takes the pulses defined in the `Processor` and
adds noise according to `Processor.noise`. It does not modify the
pulses saved in `Processor.pulses` but returns a new list.
The length of the new list of noisy pulses might be longer
because of drift Hamiltonian and device noise. They will be
added to the end of the pulses list.
Parameters
----------
device_noise: bool, optional
If true, include pulse independent noise such as single qubit
Relaxation. Default is False.
drift: bool, optional
If true, include drift Hamiltonians. Default is False.
Returns
-------
noisy_pulses: list of :class"`qutip.qip.Pulse`/:class:`qutip.qip.Drift`
A list of noisy pulses.
"""
pulses = deepcopy(self.pulses)
noisy_pulses = process_noise(pulses,
self.noise,
self.dims,
t1=self.t1,
t2=self.t2,
device_noise=device_noise)
if drift:
noisy_pulses += [self.drift]
return noisy_pulses
def get_qobjevo(self, args=None, noisy=False):
"""
Create a :class:`qutip.QobjEvo` representation of the evolution.
It calls the method `get_noisy_pulses` and create the `QobjEvo`
from it.
Parameters
----------
args: dict, optional
Arguments for :class:`qutip.QobjEvo`
noisy: bool, optional
If noise are included. Default is False.
Returns
-------
qobjevo: :class:`qutip.QobjEvo`
The :class:`qutip.QobjEvo` representation of the unitary evolution.
c_ops: list of :class:`qutip.QobjEvo`
A list of lindblad operators is also returned. if ``noisy==Flase``,
it is always an empty list.
"""
# TODO test it for non array-like coeff
# check validity
self._is_pulses_valid()
if args is None:
args = {}
else:
args = args
# set step function
if not noisy:
dynamics = self.pulses
else:
dynamics = self.get_noisy_pulses(device_noise=True, drift=True)
qu_list = []
c_ops = []
for pulse in dynamics:
if noisy:
qu, new_c_ops = pulse.get_noisy_qobjevo(dims=self.dims)
c_ops += new_c_ops
else:
qu = pulse.get_ideal_qobjevo(dims=self.dims)
qu_list.append(qu)
final_qu = _merge_qobjevo(qu_list)
final_qu.args.update(args)
if noisy:
return final_qu, c_ops
else:
return final_qu, []
def run_analytically(self, init_state=None, qc=None):
"""
Simulate the state evolution under the given `qutip.QubitCircuit`
with matrice exponentiation. It will calculate the propagator
with matrix exponentiation and return a list of :class:`qutip.Qobj`.
This method won't include noise or collpase.
Parameters
----------
qc: :class:`qutip.qip.QubitCircuit`, optional
Takes the quantum circuit to be implemented. If not given, use
the quantum circuit saved in the processor by ``load_circuit``.
init_state: :class:`qutip.Qobj`, optional
The initial state of the qubits in the register.
Returns
-------
evo_result: :class:`qutip.Result`
An instance of the class
:class:`qutip.Result` will be returned.
"""
if init_state is not None:
U_list = [init_state]
else:
U_list = []
tlist = self.get_full_tlist()
# TODO replace this by get_complete_coeff
coeffs = self.get_full_coeffs()
for n in range(len(tlist) - 1):
H = sum(
[coeffs[m, n] * self.ctrls[m] for m in range(len(self.ctrls))])
dt = tlist[n + 1] - tlist[n]
U = (-1j * H * dt).expm()
U = self.eliminate_auxillary_modes(U)
U_list.append(U)
try: # correct_global_phase are defined for ModelProcessor
if self.correct_global_phase and self.global_phase != 0:
U_list.append(globalphase(self.global_phase, N=self.N))
except AttributeError:
pass
return U_list
def run(self, qc=None):
"""
Calculate the propagator of the evolution by matrix exponentiation.
This method won't include noise or collpase.
Parameters
----------
qc: :class:`qutip.qip.QubitCircuit`, optional
Takes the quantum circuit to be implemented. If not given, use
the quantum circuit saved in the processor by `load_circuit`.
Returns
-------
U_list: list
The propagator matrix obtained from the physical implementation.
"""
if qc:
self.load_circuit(qc)
return self.run_analytically(qc=qc, init_state=None)
def run_state(self,
init_state=None,
analytical=False,
states=None,
noisy=True,
solver="mesolve",
**kwargs):
"""
If `analytical` is False, use :func:`qutip.mesolve` to
calculate the time of the state evolution
and return the result. Other arguments of mesolve can be
given as keyword arguments.
If `analytical` is True, calculate the propagator
with matrix exponentiation and return a list of matrices.
Noise will be neglected in this option.
Parameters
----------
init_state: Qobj
Initial density matrix or state vector (ket).
analytical: bool
If True, calculate the evolution with matrices exponentiation.
states: :class:`qutip.Qobj`, optional
Old API, same as init_state.
solver: str
"mesolve" or "mcsolve"
**kwargs
Keyword arguments for the qutip solver.
Returns
-------
evo_result: :class:`qutip.Result`
If ``analytical`` is False, an instance of the class
:class:`qutip.Result` will be returned.
If ``analytical`` is True, a list of matrices representation
is returned.
"""
if states is not None:
warnings.warn(
"states will be deprecated and replaced by init_state",
DeprecationWarning)
if init_state is None and states is None:
raise ValueError("Qubit state not defined.")
elif init_state is None:
# just to keep the old parameters `states`,
# it is replaced by init_state
init_state = states
if analytical:
if kwargs or self.noise:
raise warnings.warn("Analytical matrices exponentiation"
"does not process noise or"
"any keyword arguments.")
return self.run_analytically(init_state=init_state)
# kwargs can not contain H or tlist
if "H" in kwargs or "tlist" in kwargs:
raise ValueError(
"`H` and `tlist` are already specified by the processor "
"and can not be given as a keyword argument")
# construct qobjevo for unitary evolution
if "args" in kwargs:
noisy_qobjevo, sys_c_ops = self.get_qobjevo(args=kwargs["args"],
noisy=noisy)
else:
noisy_qobjevo, sys_c_ops = self.get_qobjevo(noisy=noisy)
# add collpase operators into kwargs
if "c_ops" in kwargs:
if isinstance(kwargs["c_ops"], (Qobj, QobjEvo)):
kwargs["c_ops"] += [kwargs["c_ops"]] + sys_c_ops
else:
kwargs["c_ops"] += sys_c_ops
else:
kwargs["c_ops"] = sys_c_ops
# choose solver:
if solver == "mesolve":
solver = mesolve
elif solver == "mcsolve":
solver = mcsolve
evo_result = solver(H=noisy_qobjevo,
rho0=init_state,
tlist=noisy_qobjevo.tlist,
**kwargs)
return evo_result
def load_circuit(self, qc):
"""
Translate an :class:`qutip.qip.QubitCircuit` to its
corresponding Hamiltonians. (Defined in subclasses)
"""
raise NotImplementedError("Use the function in the sub-class")
def eliminate_auxillary_modes(self, U):
"""
Eliminate the auxillary modes like the cavity modes in cqed.
(Defined in subclasses)
"""
return U
def get_operators_labels(self):
"""
Get the labels for each Hamiltonian.
It is used in the method``plot_pulses``.
It is a 2-d nested list, in the plot,
a different color will be used for each sublist.
"""
label_list = []
for pulse in self.pulses:
label_list.append(pulse.label)
return [label_list]
def plot_pulses(self,
title=None,
figsize=(12, 6),
dpi=None,
show_axis=False,
rescale_pulse_coeffs=True,
num_steps=1000):
"""
Plot the ideal pulse coefficients.
Parameters
----------
title: str, optional
Title for the plot.
figsize: tuple, optional
The size of the figure.
dpi: int, optional
The dpi of the figure.
show_axis: bool, optional
If the axis are shown.
rescale_pulse_coeffs: bool, optional
Rescale the hight of each pulses.
num_steps: int, optional
Number of time steps in the plot.
Returns
-------
fig: matplotlib.figure.Figure
The `Figure` object for the plot.
ax: matplotlib.axes._subplots.AxesSubplot
The axes for the plot.
Notes
-----
``plot_pulses`` only works for array_like coefficients
"""
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
color_list = plt.rcParams['axes.prop_cycle'].by_key()['color']
# create a axis for each pulse
fig = plt.figure(figsize=figsize, dpi=dpi)
grids = gridspec.GridSpec(len(self.pulses), 1)
grids.update(wspace=0., hspace=0.)
tlist = np.linspace(0., self.get_full_tlist()[-1], num_steps)
dt = tlist[1] - tlist[0]
# make sure coeffs start and end with zero, for ax.fill
tlist = np.hstack(([-dt * 1.e-20], tlist, [tlist[-1] + dt * 1.e-20]))
coeffs = []
for pulse in self.pulses:
coeffs.append(_pulse_interpolate(pulse, tlist))
pulse_ind = 0
axis = []
for i, label_group in enumerate(self.get_operators_labels()):
for j, label in enumerate(label_group):
grid = grids[pulse_ind]
ax = plt.subplot(grid)
axis.append(ax)
ax.fill(tlist, coeffs[pulse_ind], color_list[i], alpha=0.7)
ax.plot(tlist, coeffs[pulse_ind], color_list[i])
if rescale_pulse_coeffs:
ymax = np.max(np.abs(coeffs[pulse_ind])) * 1.1
else:
ymax = np.max(np.abs(coeffs)) * 1.1
if ymax != 0.:
ax.set_ylim((-ymax, ymax))
# disable frame and ticks
ax.set_xticks([])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.set_yticks([])
ax.set_ylabel(label, rotation=0)
pulse_ind += 1
if i == 0 and j == 0 and title is not None:
ax.set_title(title)
fig.tight_layout()
return fig, axis