Accelerated Newton-Raphson GRAPE methods for optimal control

TL;DR

This paper introduces accelerated Hessian-based Newton-Raphson GRAPE methods in Liouville space, significantly reducing computation time for quantum optimal control by leveraging auxiliary matrix and ESCALADE techniques.

Contribution

It presents a novel Hessian-based approach that improves the efficiency of Newton-Raphson GRAPE methods using two exact derivative techniques, achieving substantial speedups.

Findings

Auxiliary matrix Hessian is 4-200 times faster.

ESCALADE Hessian is 70-600 times faster.

Method reduces polynomial scaling of computation time.

Abstract

A Hessian based optimal control method is presented in Liouville space to mitigate previously undesirable polynomial scaling of computation time. This new method, an improvement to the state-of-the-art Newton-Raphson GRAPE method, is derived with respect to two exact time-propagator derivative techniques: auxiliary matrix and ESCALADE methods. We observed that compared to the best current implementation of Newton-Raphson GRAPE method, for an ensemble of 2-level systems, with realistic conditions, the new auxiliary matrix and ESCALADE Hessians can be 4-200 and 70-600 times faster, respectively.