ParaQeet - A quantum optimal control toolkit with simple parameter management#
Choose a pulse parametrization, simulate a quantum system, and optimize.
ParaQeet combines quantum optimal control methods with automatic differentiation via JAX, aimed at resource efficient computation. Currently implemented optimization methods:
GRAPE [1]: Gradient Ascent Pulse Engineering
GOAT [2]: Gradient Optimization of Analytic conTrols
dCRAB [3]: (Gradient based) dressed Chopped RAndom Basis
GOAToverGRAPE: A variant of GROUP [4] that optimizes continuous pulse parameters with GRAPE inside.
AD: Automatic differentiation of the state/propagator evolution
Currently implemented propagation methods:
Expm: Matrix exponential using JAX
expm[5]ExpmChebyshev: Matrix exponential using Chebyshev polynomial expansion [6]
ODE solvers: Diffrax [7], Verner 7th order method [8], and Scipy Runge-Kutta methods [9].
The propagation methods can be combined with the QOC methods leading to combinations such as GOAT QOC using ExpmChebyshev.
Install ParaQeet from PyPI or set up a development environment.
Quickly setup of an optimization problem with ParaQeet.
A walkthrough of the capabilities of ParaQeet with physically motivated problems.
Want to implement your own methods? Use our template base classes for an easy setup.
Full documentation of every module, class, and function.