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

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Phase retrieval via gain-based photonic XY-Hamiltonian optimization.

Richard Zhipeng Wang¹, Guangyao Li¹, Silvia Gentilini²

¹Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

Communications Physics

|March 16, 2026

Summary

This summary is machine-generated.

We present a novel photonic network approach for phase retrieval from coded diffraction patterns (CDP). This method reformulates the problem as minimizing an XY Hamiltonian, offering faster and more energy-efficient solutions than traditional algorithms.

Keywords:

Applied mathematics Applied optics Information theory and computation

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Area of Science:

Computational physics
Optics
Image processing

Background:

Phase retrieval from coded diffraction patterns (CDP) is crucial for X-ray crystallography, diffraction tomography, and astronomical imaging.
CDP recovery is a challenging, non-convex inverse problem that limits current applications.

Purpose of the Study:

To reformulate the CDP recovery problem as a minimization of a continuous-variable XY Hamiltonian.
To demonstrate the efficacy of gain-based photonic networks for solving this reformulated problem.

Main Methods:

The study leverages coupled-mode equations from exciton-polariton condensate lattices and driven photon Bose-Einstein condensates.
A spatial photonic Ising machine with high-speed digital feedback is utilized to implement the update rule, maintaining optical parallelism.

Main Results:

The gain-based photonic solver consistently outperforms the state-of-the-art Relaxed-Reflect-Reflect (RRR) algorithm in medium-noise conditions (10-40 dB SNR).
Performance advantages are maintained as problem size scales, demonstrated on diverse datasets including images, vortices, and complex data.

Conclusions:

The proposed photonic approach offers a promising solution for fast and energy-efficient phase retrieval.
Readily available photonic hardware can be employed for continuous optimization, advancing the field of phase retrieval.