用生成流网络优化量子哈密顿量分组，减少测量次数。
GFlowNets for Hamiltonian decomposition in groups of compatible operators
- 用GFlowNets构建概率框架分组可交换项
- 相比贪心着色算法测量数降低51%（完全交换）和67%（单量子比特交换）
- 适用于量子线路设计等其他资源优化问题
量子计算为直接模拟量子系统提供了前景，有望解决经典方法无法处理的化学问题。然而，当前量子算法受限于硬件条件，且实现化学精度所需测量次数过多。为缓解测量挑战，已有基于启发式方法的同类项分组技术，用于减少近中期量子设备上的测量需求。本文提出一种基于GFlowNets的概率框架，用于对给定哈密顿量中的完全交换（FC）或单量子比特交换（QWC）项进行分组。实验表明，该方法在FC与QWC分组上分别相较贪心着色算法实现了51%和67%的测量次数降低，凸显了GFlowNets在测量优化中的潜力。此外，该算法的灵活性使其可推广至哈密顿量模拟中的其他资源优化问题，如电路设计。
原文摘要 · Abstract (English)
Quantum computing presents a promising alternative for the direct simulation of quantum systems with the potential to explore chemical problems beyond the capabilities of classical methods. However, current quantum algorithms are constrained by hardware limitations and the increased number of measurements required to achieve chemical accuracy. To address the measurement challenge, techniques for grouping commuting and anti-commuting terms, driven by heuristics, have been developed to reduce the number of measurements needed in quantum algorithms on near-term quantum devices. In this work, we propose a probabilistic framework using GFlowNets to group fully (FC) or qubit-wise commuting (QWC) terms within a given Hamiltonian. The significance of this approach is demonstrated by the reduced number of measurements for the found groupings; 51% and 67% reduction factors respectively for FC and QWC partitionings with respect to greedy coloring algorithms, highlighting the potential of GFlowNets for future applications in the measurement problem. Furthermore, the flexibility of our algorithm extends its applicability to other resource optimization problems in Hamiltonian simulation, such as circuit design.
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