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Low-Rank Combinatorial Optimization and Statistical Learning by Spatial Photonic Ising Machine.

Hiroshi Yamashita¹, Ken-Ichi Okubo¹, Suguru Shimomura¹

¹Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan.

Physical Review Letters

|August 25, 2023

Summary

This summary is machine-generated.

A new spatial photonic Ising machine (SPIM) model expands capabilities beyond rank-one matrices. This advancement enables efficient solutions for diverse combinatorial optimization and machine learning problems, enhancing practical applicability.

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Area of Science:

Quantum computing
Optical computing
Computational physics

Background:

The spatial photonic Ising machine (SPIM) is an optical architecture for solving combinatorial optimization problems.
The original SPIM design was limited to Ising problems with rank-one interaction matrices.

Purpose of the Study:

To propose a novel computing model for the SPIM capable of handling any Ising problem.
To enhance the practical applicability of SPIM for broader combinatorial optimization and statistical learning tasks.

Main Methods:

Introduced a new computing model for the spatial photonic Ising machine.
Demonstrated the model's efficiency with low-rank interaction matrices, such as those in knapsack problems.
Integrated Boltzmann machine learning capabilities into the SPIM model.

Main Results:

The proposed SPIM model accommodates any Ising problem without altering the optical setup.
Achieved efficient learning, classification, and sampling of MNIST handwritten digits using the low-rank interaction model.
Showcased enhanced practical applicability for combinatorial optimization and statistical learning.

Conclusions:

The enhanced SPIM model significantly broadens the scope of solvable problems.
The model retains the inherent scalability of the SPIM architecture.
This development paves the way for more versatile optical computing solutions.