Multiparameter estimation along quantum trajectories with sequential Monte Carlo methods

Jason F. Ralph, Simon Maskell, and Kurt Jacobs
Phys. Rev. A 96, 052306 – Published 2 November 2017

Abstract

This paper proposes an efficient method for the simultaneous estimation of the state of a quantum system and the classical parameters that govern its evolution. This hybrid approach benefits from efficient numerical methods for the integration of stochastic master equations for the quantum system, and efficient parameter estimation methods from classical signal processing. The classical techniques use sequential Monte Carlo (SMC) methods, which aim to optimize the selection of points within the parameter space, conditioned by the measurement data obtained. We illustrate these methods using a specific example, an SMC sampler applied to a nonlinear system, the Duffing oscillator, where the evolution of the quantum state of the oscillator and three Hamiltonian parameters are estimated simultaneously.

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  • Received 18 July 2017

DOI:https://doi.org/10.1103/PhysRevA.96.052306

©2017 American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & TechnologyGeneral Physics

Authors & Affiliations

Jason F. Ralph1,*, Simon Maskell1,†, and Kurt Jacobs2,3,4,‡

  • 1Department of Electrical Engineering and Electronics, University of Liverpool, Brownlow Hill, Liverpool L69 3GJ, United Kingdom
  • 2U.S. Army Research Laboratory, Computational and Information Sciences Directorate, Adelphi, Maryland 20783, USA
  • 3Department of Physics, University of Massachusetts at Boston, Boston, Massachusetts 02125, USA
  • 4Hearne Institute for Theoretical Physics, Louisiana State University, Baton Rouge, Louisiana 70803, USA

  • *jfralph@liverpool.ac.uk
  • smaskell@liverpool.ac.uk
  • kurt.jacobs@umb.edu

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Issue

Vol. 96, Iss. 5 — November 2017

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