Mean-field Coherent Ising Machines with artificial Zeeman terms

TL;DR

This paper explores implementing Zeeman terms in mean-field Coherent Ising Machines, demonstrating that the chaotic amplitude control method outperforms other techniques in both simplified and physically accurate models, enabling efficient large-scale optimization.

Contribution

It introduces an efficient approach for realizing Zeeman terms in mean-field CIMs and compares the CAC method's performance to other techniques across different models.

Findings

CAC method outperforms other Zeeman realization techniques

Mean-field CIM model effectively simulates Zeeman terms

Performance consistency between simplified and accurate models

Abstract

Coherent Ising Machine (CIM) is a network of optical parametric oscillators that solves combinatorial optimization problems by finding the ground state of an Ising Hamiltonian. In CIMs, a problem arises when attempting to realize the Zeeman term because of the mismatch in size between interaction and Zeeman terms due to the variable amplitude of the optical parametric oscillator pulses corresponding to spins. There have been three approaches proposed so far to address this problem for CIM, including the absolute mean amplitude method, the auxiliary spin method, and the chaotic amplitude control (CAC) method. This paper focuses on the efficient implementation of Zeeman terms within the mean-field CIM model, which is a physics-inspired heuristic solver without quantum noise. With the mean-field model, computation is easier than with more physically accurate models, which makes it suitable for implementation in field programmable gate arrays (FPGAs) and large-scale simulations. Firstly, we examined the performance of the mean-field CIM model for realizing the Zeeman term with the CAC method, as well as their performance when compared to a more physically accurate model. Next, we compared the CAC method to other Zeeman term realization techniques on the mean-field model and a more physically accurate model. In both models, the CAC method outperformed the other methods while retaining similar performance.