Coherent Ising machines -- Quantum optics and neural network perspectives

TL;DR

This paper explores quantum optical and neural network perspectives on coherent Ising machines, addressing challenges in reaching true ground states and proposing quantum noise and feedback correction methods to improve optimization performance.

Contribution

It introduces two approaches—using quantum noise correlations and real-time error correction—to enhance CIM's ability to find true ground states.

Findings

Quantum noise can help access near-ground states.

Error correction enables better exploration of local minima.

Analogies with classical algorithms are discussed.

Abstract

A coherent Ising machine (CIM) is a network of optical parametric oscillators (OPOs), in which the "strongest" collective mode of oscillation at well above threshold corresponds to an optimum solution of a given Ising problem. When a pump rate or network coupling rate is increased from below to above threshold, however, the eigenvectors with a smallest eigenvalue of Ising coupling matrix [J_ij] appear near threshold and impede the machine to relax to true ground states. Two complementary approaches to attack this problem are described here. One approach is to utilize squeezed/anti-squeezed vacuum noise of OPOs below threshold to produce coherent spreading over numerous local minima via quantum noise correlation, which could enable the machine to access either true ground states or excited states with eigen-energies close enough to that of ground states above threshold. The other approach is to implement real-time error correction feedback loop so that the machine migrates from one local minimum to another during an explorative search for ground states. Finally, a set of qualitative analogies connecting the CIM and traditional computer science techniques are pointed out. In particular, belief propagation and survey propagation used in combinatorial optimization are touched upon.