Quantum Pareto Optimal Control

TL;DR

This paper introduces algorithms and experimental strategies for efficiently finding and exploring Pareto optimal solutions in quantum control problems involving multiple observables, improving over traditional methods.

Contribution

It presents novel algorithms that directly target the Pareto front in multiobservable quantum control, enabling continuous exploration of solution families.

Findings

Algorithms effectively locate Pareto fronts in quantum control landscapes.

Experimental strategies demonstrate practical applicability.

Methods are adaptable to other multiobservable quantum tasks.

Abstract

We describe algorithms, and experimental strategies, for the Pareto optimal control problem of simultaneously driving an arbitrary number of quantum observable expectation values to their respective extrema. Conventional quantum optimal control strategies are less effective at sampling points on the Pareto frontier of multiobservable control landscapes than they are at locating optimal solutions to single observable control problems. The present algorithms facilitate multiobservable optimization by following direct paths to the Pareto front, and are capable of continuously tracing the front once it is found to explore families of viable solutions. The numerical and experimental methodologies introduced are also applicable to other problems that require the simultaneous control of large numbers of observables, such as quantum optimal mixed state preparation.