Feedback and constraints in physical optimizers

Niharika Gunturu; Hideo Mabuchi; Edwin Ng; Daniel Wennberg; Ryotatsu Yanagimoto

doi:10.1117/12.3005007

13 March 2024 Feedback and constraints in physical optimizers

Niharika Gunturu, Hideo Mabuchi, Edwin Ng, Daniel Wennberg, Ryotatsu Yanagimoto

Proceedings Volume 12903, AI and Optical Data Sciences V; 129030K (2024) https://doi.org/10.1117/12.3005007
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

Extremizing a quadratic form can be computationally straightforward or difficult depending on the feasible domain over which variables are optimized. For example, maximizing E = x^TVx for a real-symmetric matrix 𝑉 with 𝑥 constrained to a unit ball in 𝑅^𝑁 can be performed simply by finding the maximum (principal) eigenvector of 𝑉, but can become computationally intractable if the domain of 𝑥 is limited to corners of the ±1 hypercube in 𝑅^𝑁 (i.e., 𝑥 is constrained to be a binary vector). Many gain-loss physical systems, such as coherently coupled arrays of lasers or optical parametric oscillators, naturally solve minimum/maximum eigenvector problems (of a matrix of coupling coefficients) in their equilibration dynamics. In this paper we discuss recent case studies on the use of added nonlinear dynamics and real-time feedback to enforce constraints in such systems, making them potentially useful for solving difficult optimization problems. We consider examples in both classical and quantum regimes of operation.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Niharika Gunturu, Hideo Mabuchi, Edwin Ng, Daniel Wennberg, and Ryotatsu Yanagimoto "Feedback and constraints in physical optimizers", Proc. SPIE 12903, AI and Optical Data Sciences V, 129030K (13 March 2024); https://doi.org/10.1117/12.3005007

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