量子测量设计的生成式学习
Generative Learning for Quantum Measurement Design
- Mila – Québec AI Institute（米拉-魁北克人工智能研究所）
- Université de Montréal（蒙特利尔大学）
- Université de Sherbrooke（舍布鲁克大学）
- Harvard University（哈佛大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出FlowMeas，将资源受限的量子测量设计转化为生成式学习问题，其在分子基准测试中优于现有方法，可复用策略加速再训练，还扩展到54量子比特模型，为量子测量设计提供灵活统一框架。
AI中文摘要：
从量子态中提取量子信息是量子计算的一项基础任务，通常需要在有限测量预算下估计大量非对易可观测量。对于近期有噪中等规模量子（NISQ）设备和早期容错场景，测量协议必须在统计效率与电路深度、连通性、纠缠门数量等实现资源之间取得平衡。现有许多策略聚焦于两个极端：硬件友好的乘积测量（采样成本高）和完全对易测量（电路深度大）。本文将资源受限的测量设计重新表述为一个生成式学习问题，提出FlowMeas，它使用生成式流网络直接采样受预设 shots 预算和硬件约束的浅层Clifford测量电路的有限集合。在零纠缠深度下，FlowMeas学习到的量子比特级对易测量调度在几乎所有分子基准测试中已匹配或优于领先的乘积测量方法；允许1或2个纠缠门层时，与最强的状态无关乘积测量基线相比，能量估计误差进一步降低多达27%。学习到的策略还可在相关哈密顿量之间复用，大幅加速分子势能面上的再训练；我们还获得了最多20量子比特的分子哈密顿量的结果，并将该框架应用于紧凑编码的54量子比特相互作用费米子模型，将已展示的规模扩展到了先前分子基准测试之外。这些结果确立了生成式学习作为在实际资源约束下进行量子测量设计的灵活统一框架。
英文摘要：
Extracting quantum information from a quantum state is a fundamental task of quantum computation, often requiring the estimation of many non-commuting observables under a finite measurement budget. For both near-term and early fault-tolerant settings, the measurement protocol must balance statistical efficiency against implementation resources such as circuit depth, connectivity, and entangling-gate count. Many existing strategies focus on two extremes: hardware-friendly product measurements with high sampling cost, and fully commuting measurements with deep circuits. Here we recast resource-constrained measurement design as a generative learning problem. We introduce FlowMeas, which uses a generative flow network to directly sample finite ensembles of shallow Clifford measurement circuits subject to a prescribed shot budget and hardware constraints. At zero entangling depth, FlowMeas learns qubit-wise commuting measurement schedules and already matches or improves leading product-measurement methods on nearly all molecular benchmarks. Allowing one or two entangling gate layers yields further reductions in energy estimation error of up to $27\%$ relative to the strongest state-independent product-measurement baseline. The learned policy can also be reused across related Hamiltonians, substantially accelerating retraining along a molecular potential-energy surface. We further obtain results for molecular Hamiltonians with up to 20 qubits and apply the framework to a compactly encoded 54-qubit interacting fermionic model, extending the demonstrated scale beyond prior molecular benchmarks. These results establish generative learning as a flexible and unified framework for quantum measurement design under practical resource constraints.
