Scalable Quantum-Inspired Optimization through Dynamic Qubit Compression
This addresses the hardware constraints for researchers and practitioners in quantum computing, enabling scalable optimization, though it is incremental as it builds on existing quantum-inspired methods.
The paper tackles the problem of limited qubit counts in near-term quantum devices for combinatorial optimization by introducing a quantum-inspired framework that dynamically compresses large Ising models to fit available hardware, achieving size reductions with virtually no losses in solution quality on D-wave annealers.
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.