发表机构
Fujitsu Research of India（富士通印度研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出AutoQuREO框架，通过四项核心创新实现全栈量子资源估计与优化，可作为量子计算栈的数字孪生，助力发现现有工具无法处理的资源权衡，提升量子技术就绪度。
AI 中文摘要
随着量子计算从原理验证演示向实用化推进，一个重大障碍是需要在异构硬件与软件栈层面进行系统级优化，以增强算法可行性。量子资源估计（QRE）在这一转变中发挥核心作用，但现有方法大多依赖编译或领域知识引导的符号注释，且与长期容错假设紧密耦合，限制了其主题适用性。本研究提出AutoQuREO，这是一个用于全栈量子资源估计与优化的自动化框架，其核心创新包括四点：（i）灵活的、用户可定义的量子计算栈抽象；（ii）可复用栈组件的模块化库，支持快速全栈原型设计；（iii）通过算法分析和神经符号学习实现的分层资源代理建模；（iv）将QRE直接嵌入部署流水线的集成多目标优化。这些设计选择使AutoQuREO可作为量子计算栈的数字孪生，支持对复杂设计空间的可处理探索。我们通过代表性协同设计案例研究展示AutoQuREO的能力，包括早期容错量子算法、小型纠错码、门分解及参数化量子电路的变分训练。这些示例表明，AutoQuREO能够系统发现现有QRE工具无法处理或难以理解的未利用资源权衡，AutoQuREO被定位为提升量子技术就绪度的通用平台。
英文摘要
As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks. Quantum resource estimation (QRE) plays a central role in this transition, yet existing approaches remain largely compilation-heavy or domain-knowledge-guided symbolic annotations, and tightly coupled to long-term fault-tolerant assumptions, limiting their topical applicability. In this work, we introduce AutoQuREO, an Automated framework for full-stack Quantum Resource Estimation and Optimization. AutoQuREO is built around four core novelties: (i) a flexible, user-defined abstraction of the quantum computing stack; (ii) a modular library of reusable stack components enabling rapid full-stack prototyping; (iii) surrogate modeling of layer-wise resources via algorithmic profiling and neuro-symbolic learning; and (iv) integrated multi-objective optimization that embeds QRE directly into deployment pipelines. Together, these design choices enable AutoQuREO to serve as a digital twin for quantum computing stacks, supporting the tractable exploration of complex design spaces. We demonstrate the capabilities of AutoQuREO through representative co-design case studies, including early-fault-tolerant quantum algorithms, small error correction codes, gate decomposition and variational training of parametric quantum circuits. These examples illustrate how AutoQuREO enables systematic discovery of unexploited resource trade-offs that are computationally intractable or abstruse using existing QRE tools. AutoQuREO is positioned as a general-purpose platform for advancing quantum technology readiness.
