Quantum Control Experiments as a Testbed for Evolutionary Multi-Objective Algorithms

TL;DR

This paper explores the use of evolutionary multi-objective algorithms, specifically MO-CMA-ES, in quantum control experiments, analyzing their performance in high-dimensional, noisy, and constrained quantum systems through simulations and laboratory tests.

Contribution

It introduces the concept of Multi-Observable Quantum Control (MOQC), evaluates the performance of MO-CMA-ES in experimental and simulated quantum systems, and discusses practical and theoretical implications.

Findings

MO-CMA-ES performs effectively in high-dimensional quantum control tasks.

Fitness disturbance significantly impacts algorithmic behavior.

New mathematical test-functions linked to MOQC experiments are introduced.

Abstract

Experimental multi-objective Quantum Control is an emerging topic within the broad physics and chemistry applications domain of controlling quantum phenomena. This realm offers cutting edge ultrafast laser laboratory applications, which pose multiple objectives, noise, and possibly constraints on the high-dimensional search. In this study we introduce the topic of Multi-Observable Quantum Control (MOQC), and consider specific systems to be Pareto optimized subject to uncertainty, either experimentally or by means of simulated systems. The latter include a family of mathematical test-functions with a practical link to MOQC experiments, which are introduced here for the first time. We investigate the behavior of the multi-objective version of the Covariance Matrix Adaptation Evolution Strategy (MO-CMA-ES) and assess its performance on computer simulations as well as on laboratory closed-loop experiments. Overall, we propose a comprehensive study on experimental evolutionary Pareto optimization in high-dimensional continuous domains, draw some practical conclusions concerning the impact of fitness disturbance on algorithmic behavior, and raise several theoretical issues in the broad evolutionary multi-objective context.