Speedup for quantum optimal control from automatic differentiation based on graphics processing units

TL;DR

This paper presents a GPU-accelerated quantum optimal control algorithm using automatic differentiation, enabling faster computations and more flexible, detailed control over quantum system evolution for practical experiments.

Contribution

The authors develop a GPU-based implementation of quantum optimal control leveraging automatic differentiation, significantly speeding up calculations and allowing complex optimization criteria to be incorporated easily.

Findings

GPU acceleration speeds up calculations by over an order of magnitude.

The method enables detailed control of quantum evolution at each time step.

It facilitates efficient simulations and exploration of experimental constraints.

Abstract

We implement a quantum optimal control algorithm based on automatic differentiation and harness the acceleration afforded by graphics processing units (GPUs). Automatic differentiation allows us to specify advanced optimization criteria and incorporate them in the optimization process with ease. We show that the use of GPUs can speed up calculations by more than an order of magnitude. Our strategy facilitates efficient numerical simulations on affordable desktop computers, and exploration of a host of optimization constraints and system parameters relevant to real-life experiments. We demonstrate optimization of quantum evolution based on fine-grained evaluation of performance at each intermediate time step, thus enabling more intricate control on the evolution path, suppression of departures from the truncated model subspace, as well as minimization of the physical time needed to perform high-fidelity state preparation and unitary gates.