Terminology

This is a short terminology glossary for the solver and related classes. See the rest of the guide for more in depth information.

Quantum system

Set of operators and physical parameters used to build the dynamic equations. Common components are the Hamiltonian, collapse or jump operators, bath coupling operators and environment/spectral noise configurations.

method

The identifier string used to select the numerical integration technique for a time-evolution run. May refer to an ODE or SDE integration algorithm, depending on the solver.

ODE

Ordinary Differential Equation.

$$ frac{dX}{dt} = f(t, X) $$

SDE

Stochastic Differential Equation:

$$ dX = f(t, X) dt + sum_i g_i(t, X) dW_i $$

where $dW_i$ represents an independent Wiener processes modeling Gaussian white noise.

RHS

Right-Hand Side. The representation describing the derivative in a differential equation (i.e., the $f(t, X)$ term in an ODE).

Feedback

The term we use for quantum dynamics where the underlying quantum system parameters depend explicitly on the instantaneous state of the system, introducing a form of non-linearity. For example, when its Hamiltonian depends on the expectation value of an operator or the state itself: $H = H(t, rho)$. Different Solver sub-classes may provide different feedback formats.

Deterministic / Non-deterministic

A categorization based on whether randomness is involved in the time-evolution.

  • Deterministic evolution algorithms (like standard master equations or Schrödinger equations) always yield identical results (up to numerical imprecision) across separate runs given identical initial conditions.

  • Non-deterministic evolution introduces pseudo-random variables, yielding structurally unique trajectories on successive invocations.

Stochastic

While broadly synonymous with non-deterministic behavior, in this section, “stochastic” specifically denotes simulations driven by true Stochastic Differential Equations (SDEs) containing Gaussian noise channels (e.g., SMESolver). Conversely, while Monte Carlo simulations (MCSolver) are non-deterministic due to discrete, random quantum jumps, their trajectories are piecewise-deterministic and are typically excluded when discussing “stochastic evolution”.

Trajectory

A single, continuous realization of a non-deterministic quantum evolution process. For instance, an individual run of a Monte Carlo simulation contains a unique sequence of jump events and timings that defines one distinct trajectory.