发表机构
RMIT University; Data61, CSIRO; School of Computing Technologies（皇家墨尔本理工大学; 数据61，澳大利亚联邦科学与工业研究组织; 计算技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出HamQASBench基准，通过特定指纹将分子分层，用后处理程序及多分析方法评估量子架构搜索，揭示传统指标不可见的量子算法失败模式。
AI 中文摘要
量子架构搜索（QAS）为变分量子算法自动化设计参数化量子电路，现有基准按分子身份或量子比特数组织实例，不考虑哈密顿量结构，仅依赖能量精度。我们引入HamQASBench，通过特定指纹将11个分子分为五个结构层，用后处理程序及多分析方法评估，揭示传统指标不可见的失败模式。
英文摘要
Quantum architecture search for molecular ground-state estimation is commonly evaluated through energy accuracy, which does not describe circuit cost or the physical properties of the prepared state. We introduce HamQASBench, a structure-aware benchmark comprising eleven molecular Hamiltonians of up to fourteen qubits, selected using Hamiltonian and target-state properties and supplied with exact references. Its evaluation protocol combines energy accuracy and success rates with reference-relative circuit cost, local entropy profiles for non-degenerate targets, and state identification within degenerate ground subspaces. Experiments with five methods spanning four search paradigms reveal differences hidden by energy-only comparisons. On a near-product instance under the shallow search budget, the best outputs of all five methods reach chemical accuracy while using between two and sixty-two gates. Equal-energy outputs on a degenerate instance occupy distinct spin components. Local entropy profiles distinguish inaccurate outputs and show that entangling-gate counts need not reflect realized entanglement. Across the molecular instance ladder, product-state outputs can meet or miss chemical accuracy, motivating interpretation of success alongside target-state properties. These results support retaining energy as the task-success criterion while using circuit-cost and state diagnostics for more informative comparisons. The benchmark instances, references, evaluation implementation, and per-run data are released for reuse.
