dq.MEPropagatorResult

Result of the Lindblad master equation integration to obtain the propagator.

Attributes

• propagators (array of shape (..., nsave, n^2, n^2)) –

  Saved propagators with nsave = ntsave, or nsave = 1 if options.save_states is set to False.

• final_propagator (array of shape (..., n^2, n^2)) –

  Saved final propagator.

• 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 propagators are batched according to the leading dimensions of the Hamiltonian H and jump operators jump_ops. The behaviour depends on the value of the cartesian_batching option

If cartesian_batching = True (default value)If cartesian_batching = False

The results leading dimensions are

... = ...H, ...L0, ...L1, (...)

For example if:

• H has shape (2, 3, n, n),
• jump_ops = [L0, L1] has shape [(4, 5, n, n), (6, n, n)],

then propagators has shape (2, 3, 4, 5, 6, ntsave, n, n).

The results leading dimensions are

... = ...H = ...L0 = ...L1 = (...)  # (once broadcasted)

For example if:

• H has shape (2, 3, n, n),
• jump_ops = [L0, L1] has shape [(3, n, n), (2, 1, n, n)],

then propagators has shape (2, 3, ntsave, n, n).

See the Batching simulations tutorial for more details.