Scalable Quantum-Inspired Optimization through Dynamic Qubit Compression

TL;DR

This paper introduces a quantum-inspired framework that dynamically compresses large Ising models to fit current quantum hardware, enabling more effective optimization without sacrificing solution quality.

Contribution

It proposes a physics-inspired GNN-based method for size reduction of Ising models, bridging the gap between large-scale problems and limited quantum hardware capabilities.

Findings

Effective size reduction with minimal solution loss

Compatible with the latest D-wave quantum annealers

Provides a flexible trade-off between quality and compression

Abstract

Hard combinatorial optimization problems, often mapped to Ising models, promise potential solutions with quantum advantage but are constrained by limited qubit counts in near-term devices. We present an innovative quantum-inspired framework that dynamically compresses large Ising models to fit available quantum hardware of different sizes. Thus, we aim to bridge the gap between large-scale optimization and current hardware capabilities. Our method leverages a physics-inspired GNN architecture to capture complex interactions in Ising models and accurately predict alignments among neighboring spins (aka qubits) at ground states. By progressively merging such aligned spins, we can reduce the model size while preserving the underlying optimization structure. It also provides a natural trade-off between the solution quality and size reduction, meeting different hardware constraints of quantum computing devices. Extensive numerical studies on Ising instances of diverse topologies show that our method can reduce instance size at multiple levels with virtually no losses in solution quality on the latest D-wave quantum annealers.