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Continuous-variable Quantum Phase Estimation based on Machine Learning.

Tailong Xiao1, Jingzheng Huang1, Jianping Fan2

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This study introduces a machine learning algorithm for quantum phase estimation using a Mach-Zehnder interferometer. The novel approach enhances precision and extends the estimation range, even with photon loss.

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Area of Science:

  • Quantum optics
  • Quantum information science
  • Machine learning applications

Background:

  • Photon loss is a fundamental challenge in quantum interferometry.
  • Accurate quantum phase estimation is crucial for quantum technologies.
  • Existing methods face limitations in precision, range, and robustness to noise.

Purpose of the Study:

  • To develop an efficient quantum phase estimation algorithm for continuous-variable systems.
  • To address the impact of photon loss in Mach-Zehnder interferometers.
  • To improve the precision and range of quantum phase estimation using machine learning.

Main Methods:

  • Utilizing a general physical model of a Mach-Zehnder interferometer with photon loss.
  • Developing a recursive Bayesian estimation algorithm tailored for Gaussian states.
  • Applying a machine learning approach to quantum phase estimation.

Main Results:

  • The proposed algorithm significantly improves phase estimation performance.
  • Achieved physical limits (standard quantum limit and Heisenberg limit) more efficiently.
  • Extended the phase parameter estimation range from [0, π/2] to [0, 2π].
  • Dramatically suppressed the influence of photon loss on estimation precision.

Conclusions:

  • The developed recursive Bayesian algorithm offers superior quantum phase estimation.
  • This machine learning-based approach effectively mitigates photon loss effects.
  • The algorithm is adaptable for time-variable and multi-parameter estimation frameworks.