dq.dsmesolve
dsmesolve(
H: QArrayLike | TimeQArray,
jump_ops: list[QArrayLike | TimeQArray],
etas: ArrayLike,
rho0: QArrayLike,
tsave: ArrayLike,
keys: PRNGKeyArray,
*,
exp_ops: list[QArrayLike] | None = None,
solver: Solver | None = None,
gradient: Gradient | None = None,
options: Options = Options()
) -> DSMESolveResult
Solve the diffusive stochastic master equation (SME).
The diffusive SME describes the evolution of a quantum system measured by a diffusive detector (for example homodyne or heterodyne detection in quantum optics). This function computes the evolution of the density matrix \(\rho(t)\) at time \(t\), starting from an initial state \(\rho_0\), according to the diffusive SME in ItΓ΄ form (\(\hbar=1\), time is implicit(1)) $$ \begin{split} \dd\rho =&~ -i[H, \rho]\,\dt + \sum_{k=1}^N \left( L_k \rho L_k^\dag - \frac{1}{2} L_k^\dag L_k \rho - \frac{1}{2} \rho L_k^\dag L_k \right)\dt \\ &+ \sum_{k=1}^N \sqrt{\eta_k} \left( L_k \rho + \rho L_k^\dag - \tr{(L_k+L_k^\dag)\rho}\rho \right)\dd W_k, \end{split} $$ where \(H\) is the system's Hamiltonian, \(\{L_k\}\) is a collection of jump operators, each continuously measured with efficiency \(0\leq\eta_k\leq1\) (\(\eta_k=0\) for purely dissipative loss channels) and \(\dd W_k\) are independent Wiener processes.
- With explicit time dependence:
- \(\rho\to\rho(t)\)
- \(H\to H(t)\)
- \(L_k\to L_k(t)\)
- \(\dd W_k\to \dd W_k(t)\)
The continuous-time measurements are defined with the ItΓ΄ processes \(\dd Y_k\) (time is implicit) $$ \dd Y_k = \sqrt{\eta_k} \tr{(L_k + L_k^\dag) \rho} \dt + \dd W_k. $$
The solver returns the time-averaged measurements \(I_k(t_n, t_{n+1})\) defined for
each time interval \([t_n, t_{n+1}[\) by
$$
I_k(t_n, t_{n+1}) = \frac{Y_k(t_{n+1}) - Y_k(t_n)}{t_{n+1} - t_n}
= \frac{1}{t_{n+1}-t_n}\int_{t_n}^{t_{n+1}} \dd Y_k(t)
$$
The time intervals \([t_n, t_{n+1}[\) are defined by tsave
, so the number of
returned measurement values for each detector is len(tsave)-1
.
Warning
For now, dsmesolve()
only supports linearly spaced tsave
with values that
are exact multiples of the solver fixed step size dt
.
Parameters
-
H
(qarray-like or time-qarray of shape (...H, n, n))
β
Hamiltonian.
-
jump_ops
(list of qarray-like or time-qarray, each of shape (n, n))
β
List of jump operators.
-
etas
(array-like of shape (len(jump_ops),))
β
Measurement efficiency for each loss channel with values between 0 (purely dissipative) and 1 (perfectly measured). No measurement is returned for purely dissipative loss channels.
-
rho0
(qarray-like of shape (...rho0, n, 1) or (...rho0, n, n))
β
Initial state.
-
tsave
(array-like of shape (ntsave,))
β
Times at which the states and expectation values are saved. The equation is solved from
tsave[0]
totsave[-1]
. Measurements are time-averaged and saved over each interval defined bytsave
. -
keys
(list of PRNG keys)
β
PRNG keys used to sample the Wiener processes. The number of elements defines the number of sampled stochastic trajectories.
-
exp_ops
(list of array-like, each of shape (n, n), optional)
β
List of operators for which the expectation value is computed.
-
solver
β
Solver for the integration. No defaults for now, you have to specify a solver (supported:
EulerMaruyama
). -
gradient
β
Algorithm used to compute the gradient. The default is solver-dependent, refer to the documentation of the chosen solver for more details.
-
options
β
Generic options (supported:
save_states
,cartesian_batching
,save_extra
).Detailed options API
dq.Options( save_states: bool = True, cartesian_batching: bool = True, save_extra: callable[[Array], PyTree] | None = None, )
Parameters
- save_states - If
True
, the state is saved at every time intsave
, otherwise only the final state is returned. - cartesian_batching - If
True
, batched arguments are treated as separated batch dimensions, otherwise the batching is performed over a single shared batched dimension. - save_extra (function, optional) - A function with signature
f(QArray) -> PyTree
that takes a state as input and returns a PyTree. This can be used to save additional arbitrary data during the integration, accessible inresult.extra
.
- save_states - If
Returns
dq.DSMESolveResult
object holding the result of the diffusive SME integration.
Use result.states
to access the saved states, result.expects
to access
the saved expectation values and result.measurements
to access the
detector measurements.
Detailed result API
dq.DSMESolveResult
For the shape indications we define ntrajs
as the number of
trajectories (ntrajs = len(keys)
) and nLm
as the number of measured
loss channels (those for which the measurement efficiency is not zero).
Attributes
- states (qarray of shape (..., ntrajs, nsave, n, n)) - Saved
states with
nsave = ntsave
, ornsave = 1
ifoptions.save_states=False
. - final_state (qarray of shape (..., ntrajs, n, n)) - Saved final state.
- expects (array of shape (..., ntrajs, len(exp_ops), ntsave) or None) - Saved
expectation values, if specified by
exp_ops
. - measurements (array of shape (..., ntrajs, nLm, nsave-1)) - Saved measurements.
- extra (PyTree or None) - Extra data saved with
save_extra()
if specified inoptions
. - keys (PRNG key array of shape (ntrajs,)) - PRNG keys used to sample the Wiener processes.
- infos (PyTree or None) - Solver-dependent information on the resolution.
- tsave (array of shape (ntsave,)) - Times for which results were saved.
- solver (Solver) - Solver used.
- gradient (Gradient) - Gradient used.
- options (Options) - Options used.
Advanced use-cases
Defining a time-dependent Hamiltonian or jump operator
If the Hamiltonian or the jump operators depend on time, they can be converted
to time-arrays using dq.pwc()
,
dq.modulated()
, or
dq.timecallable()
. See the
Time-dependent operators
tutorial for more details.
Running multiple simulations concurrently
The Hamiltonian H
and the initial density matrix rho0
can be batched to
solve multiple SMEs concurrently. All other arguments (including the PRNG key)
are common to every batch. The resulting states, measurements and expectation values
are batched according to the leading dimensions of H
and rho0
. The
behaviour depends on the value of the cartesian_batching
option.
The results leading dimensions are
... = ...H, ...rho0
H
has shape (2, 3, n, n),rho0
has shape (4, n, n),
then result.states
has shape (2, 3, 4, ntrajs, ntsave, n, n).
The results leading dimensions are
... = ...H = ...rho0 # (once broadcasted)
H
has shape (2, 3, n, n),rho0
has shape (3, n, n),
then result.states
has shape (2, 3, ntrajs, ntsave, n, n).
See the Batching simulations tutorial for more details.
Warning
Batching on jump_ops
and etas
is not yet supported, if this is
needed don't hesitate to
open an issue on GitHub.