Phase Retrieval via Gain-Based Photonic XY-Hamiltonian Optimization
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918013063659520 |
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| author | Wang, Richard Zhipeng Li, Guangyao Gentilini, Silvia Strinati, Marcello Calvanese Conti, Claudio Berloff, Natalia G. |
| author_facet | Wang, Richard Zhipeng Li, Guangyao Gentilini, Silvia Strinati, Marcello Calvanese Conti, Claudio Berloff, Natalia G. |
| contents | Phase-retrieval from coded diffraction patterns (CDP) is important to X-ray crystallography, diffraction tomography and astronomical imaging, yet remains a hard, non-convex inverse problem. We show that CDP recovery can be reformulated exactly as the minimisation of a continuous-variable XY Hamiltonian and solved by gain-based photonic networks. The coupled-mode equations we exploit are the natural mean-field dynamics of exciton-polariton condensate lattices, coupled-laser arrays and driven photon Bose-Einstein condensates, while other hardware such as the spatial photonic Ising machine can implement the same update rule through high-speed digital feedback, preserving full optical parallelism. Numerical experiments on images, two- and three-dimensional vortices and unstructured complex data demonstrate that the gain-based solver consistently outperforms the state-of-the-art Relaxed-Reflect-Reflect (RRR) algorithm in the medium-noise regime (signal-to-noise ratios 10--40 dB) and retains this advantage as problem size scales. Because the physical platform performs the continuous optimisation, our approach promises fast, energy-efficient phase retrieval on readily available photonic hardware. uch as two- and three-dimensional vortices, and unstructured random data. Moreover, the solver's accuracy remains high as problem sizes increase, underscoring its scalability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04766 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Phase Retrieval via Gain-Based Photonic XY-Hamiltonian Optimization Wang, Richard Zhipeng Li, Guangyao Gentilini, Silvia Strinati, Marcello Calvanese Conti, Claudio Berloff, Natalia G. Optics Disordered Systems and Neural Networks Other Condensed Matter Emerging Technologies Phase-retrieval from coded diffraction patterns (CDP) is important to X-ray crystallography, diffraction tomography and astronomical imaging, yet remains a hard, non-convex inverse problem. We show that CDP recovery can be reformulated exactly as the minimisation of a continuous-variable XY Hamiltonian and solved by gain-based photonic networks. The coupled-mode equations we exploit are the natural mean-field dynamics of exciton-polariton condensate lattices, coupled-laser arrays and driven photon Bose-Einstein condensates, while other hardware such as the spatial photonic Ising machine can implement the same update rule through high-speed digital feedback, preserving full optical parallelism. Numerical experiments on images, two- and three-dimensional vortices and unstructured complex data demonstrate that the gain-based solver consistently outperforms the state-of-the-art Relaxed-Reflect-Reflect (RRR) algorithm in the medium-noise regime (signal-to-noise ratios 10--40 dB) and retains this advantage as problem size scales. Because the physical platform performs the continuous optimisation, our approach promises fast, energy-efficient phase retrieval on readily available photonic hardware. uch as two- and three-dimensional vortices, and unstructured random data. Moreover, the solver's accuracy remains high as problem sizes increase, underscoring its scalability. |
| title | Phase Retrieval via Gain-Based Photonic XY-Hamiltonian Optimization |
| topic | Optics Disordered Systems and Neural Networks Other Condensed Matter Emerging Technologies |
| url | https://arxiv.org/abs/2505.04766 |