动态压缩大模型，让量子硬件高效解决复杂优化问题。
Scalable Quantum-Inspired Optimization through Dynamic Qubit Compression
- 用物理启发的图神经网络预测自旋对齐，实现智能压缩
- 多层级压缩后仍保持接近最优解，损失可忽略
- 适合资源受限的量子退火设备，灵活适配不同硬件
硬性组合优化问题常被映射为伊辛模型，虽具量子优势潜力，却受近中期设备比特数限制。本文提出一种量子启发式框架，通过动态压缩大型伊辛模型以适配不同规模的量子硬件，弥合大规模优化与现有硬件能力之间的差距。方法利用物理启发的图神经网络（GNN）捕捉伊辛模型中复杂的相互作用，准确预测基态下邻近自旋（即量子比特）的对齐关系。通过逐步合并这些对齐的自旋，可在保留优化结构的前提下减小模型规模，并自然实现解质量与压缩程度的权衡，满足不同量子计算设备的硬件约束。在多种拓扑结构的伊辛实例上进行的大量数值实验表明，该方法可在多个层级实现模型压缩，且在最新的D-Wave量子退火器上几乎无解质损失。
原文摘要 · Abstract (English)
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.
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