Incoherent control of quantum systems with wavefunction-controllable subspaces via quantum reinforcement learning

Daoyi Y. Dong, Chunlin Chen, Tzyh Jong Tarn, Alexander Pechen, Herschel Rabitz

Research output: Contribution to journalArticle

48 Scopus citations

Abstract

In this paper, an incoherent control scheme for accomplishing the state control of a class of quantum systems which have wavefunction-controllable subspaces is proposed. This scheme includes the following two steps: projective measurement on the initial state and learning control in the wavefunction-controllable subspace. The first step probabilistically projects the initial state into the wavefunction-controllable subspace. The probability of success is sensitive to the initial state; however, it can be greatly improved through multiple experiments on several identical initial states even in the case with a small probability of success for an individual measurement. The second step finds a local optimal control sequence via quantum reinforcement learning and drives the controlled system to the objective state through a set of suitable controls. In this strategy, the initial states can be unknown identical states, the quantum measurement is used as an effective control, and the controlled system is not necessarily unitarily controllable. This incoherent control scheme provides an alternative quantum engineering strategy for locally controllable quantum systems.

Original languageEnglish (US)
Pages (from-to)957-962
Number of pages6
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume38
Issue number4
DOIs
StatePublished - Aug 1 2008

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Incoherent control
  • Quantum reinforcement learning (QRL)
  • Wavefunction controllability
  • Wavefunction-controllable subspace

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