用经典模型模拟量子处理器，大幅降低实验成本。
Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors
- 构建可预测的类经典代理模型，高效模拟量子处理器均值行为。
- 在20个超导量子比特上实现高效预训练与拓扑相识别。
- 适合需要大量量子资源但计算量大的研究者使用。
量子处理器的发展正推动科学突破，但大规模制造成本高昂，使其长期稀缺，限制了广泛应用。为解决这一瓶颈，我们提出预测代理模型——一类可证明计算高效的经典学习模型，用于模拟给定量子处理器的均值行为。特别地，我们设计了两种预测代理，显著减少在多种实际场景中对量子处理器访问的需求。为验证其在数字量子模拟中的潜力，我们利用这些代理模拟了最多20个可编程超导量子比特的量子处理器，实现了横场伊辛模型族的变分量子本征求解器高效预训练，并识别出非平衡弗洛凯对称性保护拓扑相。实验结果表明，预测代理不仅将测量开销降低数个数量级，还能超越传统依赖量子资源的方法性能。这些成果确立了预测代理作为拓展先进量子处理器影响力的实用路径。
原文摘要 · Abstract (English)
The ongoing development of quantum processors is driving breakthroughs in scientific discovery. Despite this progress, the formidable cost of fabricating large-scale quantum processors means they will remain rare for the foreseeable future, limiting their widespread application. To address this bottleneck, we introduce the concept of predictive surrogates, which are classical learning models designed to emulate the mean-value behavior of a given quantum processor with provably computational efficiency. In particular, we propose two predictive surrogates that can substantially reduce the need for quantum processor access in diverse practical scenarios. To demonstrate their potential in advancing digital quantum simulation, we use these surrogates to emulate a quantum processor with up to 20 programmable superconducting qubits, enabling efficient pre-training of variational quantum eigensolvers for families of transverse-field Ising models and identification of non-equilibrium Floquet symmetry-protected topological phases. Experimental results reveal that the predictive surrogates not only reduce measurement overhead by orders of magnitude, but can also surpass the performance of conventional, quantum-resource-intensive approaches. Collectively, these findings establish predictive surrogates as a practical pathway to broadening the impact of advanced quantum processors.
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