Optimal control of families of quantum gates

TL;DR

This paper introduces a neural network-based optimal control method for designing continuous families of quantum gates, enabling efficient realization of classes of operations in minimal time.

Contribution

It extends existing quantum optimal control frameworks to handle continuous families of targets, providing a novel approach for multi-target quantum gate optimization.

Findings

Neural network-based optimization can find time-dependent Hamiltonians for quantum gate families.

The method achieves minimal-time realization of desired quantum gate classes.

It generalizes single-target control to continuous families, enhancing quantum operation design.

Abstract

Quantum Optimal Control (QOC) enables the realization of accurate operations, such as quantum gates, and support the development of quantum technologies. To date, many QOC frameworks have been developed but those remain only naturally suited to optimize a single targeted operation at a time. We extend this concept to optimal control with a continuous family of targets, and demonstrate that an optimization based on neural networks can find families of time-dependent Hamiltonians that realize desired classes of quantum gates in minimal time.