dq.MESolveResult
Result of the Lindblad master equation integration.
Attributes
-
states
(array of shape (..., nsave, n, n))
–
Saved states with
nsave = ntsave
, ornsave = 1
ifoptions.save_states
is set toFalse
. -
final_state
(array of shape (..., n, n))
–
Saved final state.
-
expects
(array of shape (..., len(exp_ops), ntsave) or None)
–
Saved expectation values, if specified by
exp_ops
. -
extra
(PyTree or None)
–
Extra data saved with
save_extra()
if specified inoptions
(seedq.Options
). -
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.
Result of running multiple simulations concurrently
The resulting states and expectation values are batched according to the
leading dimensions of the Hamiltonian H
, jump operators jump_ops
and initial
state rho0
. The behaviour depends on the value of the cartesian_batching
option
The results leading dimensions are
... = ...H, ...L0, ...L1, (...), ...rho0
H
has shape (2, 3, n, n),jump_ops = [L0, L1]
has shape [(4, 5, n, n), (6, n, n)],rho0
has shape (7, n, n),
then states
has shape (2, 3, 4, 5, 6, 7, ntsave, n, n).
The results leading dimensions are
... = ...H = ...L0 = ...L1 = (...) = ...rho0 # (once broadcasted)
H
has shape (2, 3, n, n),jump_ops = [L0, L1]
has shape [(3, n, n), (2, 1, n, n)],rho0
has shape (3, n, n),
then states
has shape (2, 3, ntsave, n, n).
See the Batching simulations tutorial for more details.