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A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

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Published on: September 5, 2019

Bayesian inference for the potential in PT-symmetric optical systems.

Yedan Zhao¹, Yinghong Xu¹, Lipu Zhang²

¹Department of Mathematics, Zhejiang Sci-Tech University, Hangzhou 310018, China.

Chaos (Woodbury, N.Y.)

|May 8, 2025

Summary

This study presents a new Bayesian framework and sampling method for optical systems. It improves uncertainty quantification and aids in selecting nonlinear optical materials.

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Area of Science:

Physics
Optical Systems
Computational Science

Background:

Quantifying uncertainty in complex potentials of PT-symmetric optical systems is challenging.
Existing Bayesian inference methods may lack statistical robustness.

Purpose of the Study:

Introduce a robust Bayesian statistical framework for uncertainty quantification in PT-symmetric optical systems.
Develop an efficient and precise sampling method for posterior distribution calculation.

Main Methods:

Formulated an informative prior density function using parity and continuity.
Proposed the Modified-Landweber-Metropolis-Hastings (MLMH) method, combining modified Landweber iteration with Metropolis-Hastings.
Applied the MLMH method for calculating posterior distributions.

Main Results:

The MLMH method demonstrates higher precision compared to other modern sampling techniques.
The Bayesian framework enhances statistical robustness in inference.
Numerical experiments validate the effectiveness of the proposed methods.

Conclusions:

The developed Bayesian framework and MLMH method offer a powerful tool for analyzing PT-symmetric optical systems.
This work facilitates the selection of suitable nonlinear optical materials.
The findings advance the field of computational optics and Bayesian statistics.