Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics

TL;DR

This paper introduces a differential evolution approach with heuristics and parallelization to optimize quantum control in high-dimensional noisy systems, outperforming traditional greedy algorithms in fidelity and scalability.

Contribution

It presents a novel application of differential evolution with heuristics and parallel computing for quantum control optimization, addressing limitations of greedy algorithms.

Findings

Achieved higher fidelity in quantum phase estimation.

Demonstrated improved scalability in quantum gate design.

Outperformed greedy algorithms in noisy, high-dimensional systems.

Abstract

Quantum control is valuable for various quantum technologies such as high-fidelity gates for universal quantum computing, adaptive quantum-enhanced metrology, and ultra-cold atom manipulation. Although supervised machine learning and reinforcement learning are widely used for optimizing control parameters in classical systems, quantum control for parameter optimization is mainly pursued via gradient-based greedy algorithms. Although the quantum fitness landscape is often compatible with greedy algorithms, sometimes greedy algorithms yield poor results, especially for large-dimensional quantum systems. We employ differential evolution algorithms to circumvent the stagnation problem of non-convex optimization. We improve quantum control fidelity for noisy system by averaging over the objective function. To reduce computational cost, we introduce heuristics for early termination of runs and for adaptive selection of search subspaces. Our implementation is massively parallel and vectorized to reduce run time even further. We demonstrate our methods with two examples, namely quantum phase estimation and quantum gate design, for which we achieve superior fidelity and scalability than obtained using greedy algorithms.