dq.smesolve

smesolve(
    H: ArrayLike | TimeArray,
    jump_ops: list[ArrayLike | TimeArray],
    etas: ArrayLike,
    rho0: ArrayLike,
    tsave: ArrayLike,
    *,
    tmeas: ArrayLike | None = None,
    ntrajs: int = 10,
    exp_ops: list[ArrayLike] | None = None,
    solver: Solver | None = None,
    gradient: Gradient | None = None,
    options: Options = Options()
) -> Result

Solve the diffusive stochastic master equation (SME).

Warning

This function has not been ported to JAX yet. The following documentation is a draft API, copied from the old PyTorch version of the library.

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 Itô form (with \(\hbar=1\) and where 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.

1. With explicit time dependence:
   • \(\rho\to\rho(t)\)
   • \(H\to H(t)\)
   • \(L_k\to L_k(t)\)
   • \(\dd W_k\to \dd W_k(t)\)

Diffusive vs. jump SME

In quantum optics the diffusive SME corresponds to homodyne or heterodyne detection schemes, as opposed to the jump SME which corresponds to photon counting schemes. No solver for the jump SME is provided yet, if this is needed don't hesitate to open an issue on GitHub.

The measured signals \(I_k=\dd y_k/\dt\) verifies (again time is implicit): $$ \dd y_k =\sqrt{\eta_k} \tr{(L_k + L_k^\dag) \rho} \dt + \dd W_k. $$

Signal normalisation

Sometimes the signals are defined with a different but equivalent normalisation \(\dd y_k' = \dd y_k/(2\sqrt{\eta_k})\).

The signals \(I_k\) are singular quantities, the solver returns the averaged signals \(J_k\) defined for a time interval \([t_0, t_1)\) by: $$ J_k([t_0, t_1)) = \frac{1}{t_1-t_0}\int_{t_0}^{t_1} I_k(t)\, \dt = \frac{1}{t_1-t_0}\int_{t_0}^{t_1} \dd y_k(t). $$ The time intervals for integration are defined by the argument tmeas, which defines len(tmeas) - 1 intervals. By default, tmeas = tsave, so the signals are averaged between the times at which the states are saved.

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, the jump operators jump_ops and the initial density matrix rho0 can be batched to solve multiple SMEs concurrently. All other arguments are common to every batch. See the Batching simulations tutorial for more details.

Parameters

• H (array-like or time-array of shape (bH?, n, n)) –

  Hamiltonian.

• jump_ops (list of array-like or time-array, of shape (nL, n, n)) –

  List of jump operators.

• etas (array-like of shape (nL,)) –

  Measurement efficiencies, must be of the same length as jump_ops with values between 0 and 1. For a purely dissipative loss channel, set the corresponding efficiency to 0. No measurement signal will be returned for such channels.

• rho0 (array-like of shape (brho?, n, 1) or (brho?, n, n)) –

  Initial state.

• tsave (array-like of shape (ntsave,)) –

  Times at which the states and expectation values are saved. The equation is solved from tsave[0] to tsave[-1], or from t0 to tsave[-1] if t0 is specified in options.

• tmeas (array-like of shape (ntmeas,), optional) –

  Times between which measurement signals are averaged and saved. Defaults to tsave.

• ntrajs –

  Number of stochastic trajectories to solve concurrently.

• exp_ops (list of array-like, of shape (nE, n, n), optional) –

  List of operators for which the expectation value is computed.

• solver –

  Solver for the integration.

• gradient –

  Algorithm used to compute the gradient.

• options –

  Generic options, see dq.Options.