Variational Optical Processors

We introduce a new class of self-configuring photonic architectures called variational optical processors (VOPs). These devices tackle the problem of decomposing and measuring multimode light fields, both partially coherent and quantum, without requiring prior knowledge of the modes involved. Classical strategies for modal decomposition—such as Karhunen-Loève expansions [1] for partially coherent beams or Schmidt decompositions [2] for bipartite quantum states—often rely on comprehensive tomography and complex data processing. By contrast, VOPs discover the relevant modes in situ through a simple optimization of detection signals at their outputs (e.g., measured optical power or coincidence counts), eliminating the overhead usually associated with scanning and reconstruction. Prior demonstrations of self-configuring photonic networks have proven the feasibility of such approaches for analyzing and generating multimode coherent optical fields [3].

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Publication Details

Published
2025-06-23
DOI
https://doi.org/10.1109/cleo/europe-eqec65582.2025.11109871
Primary Topic
Neural Networks and Reservoir Computing
Type
article
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article

Variational Optical Processors

Charles Roques‐Carmes, David A. B. Miller, Aviv Karnieli, Shanhui Fan
Neural Networks and Reservoir Computing
article

Variational Optical Processors

Charles Roques‐Carmes, David A. B. Miller, Aviv Karnieli, Shanhui Fan
article en

Abstract

We introduce a new class of self-configuring photonic architectures called variational optical processors (VOPs). These devices tackle the problem of decomposing and measuring multimode light fields, both partially coherent and quantum, without requiring prior knowledge of the modes involved. Classical strategies for modal decomposition—such as Karhunen-Loève expansions [1] for partially coherent beams or Schmidt decompositions [2] for bipartite quantum states—often rely on comprehensive tomography and complex data processing. By contrast, VOPs discover the relevant modes in situ through a simple optimization of detection signals at their outputs (e.g., measured optical power or coincidence counts), eliminating the overhead usually associated with scanning and reconstruction. Prior demonstrations of self-configuring photonic networks have proven the feasibility of such approaches for analyzing and generating multimode coherent optical fields [3].

Stanford University (US)
Openalex Percentile: Top 89%
Neural Networks and Reservoir Computing
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