quchip.engine.steady_state

Build and solve backend-neutral stationary Lindblad problems.

Functions

build_steadystate_problem(chip, *[, e_ops, ...])	Resolve a chip into one static Lindblad steady-state request.
solve_steadystate_problem(problem, *[, ...])	Solve an already assembled stationary problem and enforce uniqueness.
steadystate(chip, *[, e_ops, options, ...])	Solve the unique static Lindblad steady state of a chip.
steadystate_batch(chip, *axes[, e_ops, ...])	Solve stationary states over Cartesian or zipped parameter axes.

quchip.engine.steady_state.build_steadystate_problem(chip, *, e_ops=None, options=None, frame=None, approximation=None)

Resolve a chip into one static Lindblad steady-state request.

Under an excitation cutoff, the request holds the projected model on the retained labels.

quchip.engine.steady_state.steadystate(chip, *, e_ops=None, options=None, frame=None, approximation=None)

Solve the unique static Lindblad steady state of a chip.

A conserving chip with an excitation cutoff, or with more than 64 product states, returns the vacuum on its labels with N <= 1 when the lowering channels prove it unique. Other chips, and every call with options, solve the backend’s stationary Liouvillian.

quchip.engine.steady_state.solve_steadystate_problem(problem, *, prepared=None, guess=None, reason="The request names the backend's stationary Liouvillian.")

Solve an already assembled stationary problem and enforce uniqueness.

guess is a native stationary state of a related problem. The backend can reuse it when it is also stationary here. reason explains the route in the result’s diagnostics.

quchip.engine.steady_state.steadystate_batch(chip, *axes, e_ops=None, options=None, frame=None, approximation=None, progress=True)

Solve stationary states over Cartesian or zipped parameter axes.