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Related Experiment Video

Updated: Jan 19, 2026

Principle, Conservation and Measurement of Angular Momentum

Principle, Conservation and Measurement of Angular Momentum

Published on: April 30, 2023

Binary optimization by momentum annealing.

Takuya Okuyama¹, Tomohiro Sonobe², Ken-Ichi Kawarabayashi²

¹Hitachi, Ltd., 1-280, Higashi-Koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan.

Physical Review. E

|September 11, 2019

Summary

This summary is machine-generated.

Momentum annealing (MA) is a new parallel computing algorithm for solving complex optimization problems. This GPU-accelerated method is 250x faster than simulated annealing (SA) for large Ising models.

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Area of Science:

Computational physics
Quantum computing algorithms
Optimization techniques

Background:

Combinatorial optimization problems are crucial in computing.
Simulated annealing (SA) is a common method but struggles with parallelization.
Mapping optimization to Ising models is an active research area.

Purpose of the Study:

To develop a novel, parallelizable algorithm for large-scale combinatorial optimization.
To overcome the limitations of simulated annealing in parallel processing.
To leverage GPU computing for faster optimization solutions.

Main Methods:

Proposed a new algorithm: momentum annealing (MA).
MA updates all spins of fully connected Ising models simultaneously.
Implemented MA on Graphics Processing Units (GPUs) for parallel computation.

Main Results:

MA demonstrates significant speedup compared to SA.
Achieved 250x faster performance than SA on a modern CPU.
Successfully solved 100,000-spin Ising models efficiently using parallel GPUs.

Conclusions:

Momentum annealing offers a highly efficient parallel approach for optimization.
GPU implementation of MA drastically reduces computation time for large Ising models.
MA represents a significant advancement in computational optimization strategies.