Sampling-Based Learning Control for Quantum Systems with Uncertainties

Daoyi Dong, Mohamed A. Mabrok, Ian R. Petersen, Bo Qi, Chunlin Chen, Herschel Albert Rabitz

Research output: Contribution to journalArticle

33 Scopus citations

Abstract

Robust control design for quantum systems has been recognized as a key task in the development of practical quantum technology. In this paper, we present a systematic numerical methodology of sampling-based learning control (SLC) for control design of quantum systems with uncertainties. The SLC method includes two steps of training and testing. In the training step, an augmented system is constructed using artificial samples generated by sampling uncertainty parameters according to a given distribution. A gradient flow-based learning algorithm is developed to find the control for the augmented system. In the process of testing, a number of additional samples are tested to evaluate the control performance, where these samples are obtained through sampling the uncertainty parameters according to a possible distribution. The SLC method is applied to three significant examples of quantum robust control, including state preparation in a three-level quantum system, robust entanglement generation in a two-qubit superconducting circuit, and quantum entanglement control in a two-atom system interacting with a quantized field in a cavity. Numerical results demonstrate the effectiveness of the SLC approach even when uncertainties are quite large, and show its potential for robust control design of quantum systems.

Original languageEnglish (US)
Article number7052406
Pages (from-to)2155-2166
Number of pages12
JournalIEEE Transactions on Control Systems Technology
Volume23
Issue number6
DOIs
StatePublished - Nov 1 2015

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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