Robust quantum control in games: An adversarial learning approach

Xiaozhen Ge, Haijin Ding, Herschel Rabitz, Re Bing Wu

Research output: Contribution to journalArticlepeer-review

Abstract

High-precision operation of quantum computing systems must be robust to uncertainties and noises in the quantum hardware. We show that through a game played between the uncertainties (or noises) and the controls, adversarial uncertainty samples can be generated to find highly robust controls through the search for Nash equilibria. We propose a broad family of adversarial learning algorithms, namely a-GRAPE algorithms, which includes two effective learning schemes referred to as the best-response approach and the better-response approach within game-theoretic terminology, providing options for learning highly robust controls. Numerical experiments demonstrate that the balance between fidelity and robustness depends on the details of the chosen adversarial learning algorithm, which can effectively lead to a significant enhancement of control robustness while attaining high fidelity.

Original languageEnglish (US)
Article number052317
JournalPhysical Review A
Volume101
Issue number5
DOIs
StatePublished - May 2020

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics

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