菜单

关于 🐙 GitHub
arXiv 提交日期: 2026-07-06
📄 Abstract - HamQASBench: A Hamiltonian-Informed Diagnostic Benchmark for Evaluating Quantum Architecture Search

Quantum Architecture Search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms, yet existing benchmarks organize instances by molecular identity or qubit count -- criteria agnostic to Hamiltonian structure -- and rely solely on energy accuracy, which cannot detect structural failures such as over-parameterization on near-product ground states. We introduce HamQASBench, a Hamiltonian-informed diagnostic benchmark organizing 11 molecules into five structural tiers via fingerprints derived from the Pauli operator basis, computational basis representation, and ground-state entanglement. A post-hoc critical-structure extraction procedure identifies minimal circuits consistent with each tier's requirements, complementing energy-based evaluation with per-qubit entanglement analysis and pairwise state fidelity. Benchmarking five QAS methods across four paradigms reveals failure modes invisible to conventional metrics: over-parameterization in the minimalism regime, eigenstate commitment under degeneracy, a representation bottleneck in strongly correlated systems, topology-induced routing failure, and circuit search space growth as a scalability bottleneck.

顶级标签: machine learning quantum computing
详细标签: quantum architecture search benchmark hamiltonian structure circuit evaluation failure analysis 或 搜索:

HamQASBench:一个哈密顿信息驱动的量子架构搜索诊断基准 / HamQASBench: A Hamiltonian-Informed Diagnostic Benchmark for Evaluating Quantum Architecture Search


1️⃣ 一句话总结

该论文提出了一个名为HamQASBench的基准测试,通过分析量子哈密顿量的结构特征来系统性地诊断和评估量子电路搜索算法中的多种潜在失效模式,克服了传统仅依赖能量准确率的评测方法的局限性。

源自 arXiv: 2607.04845