用可学习的量子模块提升硬件适配性，解决真实设备上的难解量子问题。
Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
- 通过程序合成生成可复用的复合门，扩大策略空间
- 在真实算力下实现10量子比特系统，精度与兼容性双提升
- 适合研究量子算法与硬件协同设计的科研人员
量子计算为模拟复杂量子系统和优化大规模组合问题提供了新机遇，但其实际应用受限于设备噪声和连接性约束。量子电路设计作为量子算法的核心挑战，在现有基于强化学习的方法中，当受限于硬件原生门和设备级编译时，准确率显著下降。本文提出一种新型方法——模块化强化学习（GRL），将学习与程序合成结合，自动构建符合硬件约束的复合门，扩展动作空间。实验表明，该方法在横向场伊辛模型和量子化学问题上均提升了准确性、硬件兼容性与可扩展性，可在合理计算预算内处理最多十量子比特系统。该框架展示了可学习、可复用的电路模块如何推动量子处理器的算法-硬件协同设计。
原文摘要 · Abstract (English)
Quantum computing offers exciting opportunities for simulating complex quantum systems and optimizing large scale combinatorial problems, but its practical use is limited by device noise and constrained connectivity. Designing quantum circuits, which are fundamental to quantum algorithms, is therefore a central challenge in current quantum hardware. Existing reinforcement learning based methods for circuit design lose accuracy when restricted to hardware native gates and device level compilation. Here, we introduce gadget reinforcement learning (GRL), which combines learning with program synthesis to automatically construct composite gates that expand the action space while respecting hardware constraints. We show that this approach improves accuracy, hardware compatibility, and scalability for transverse-field Ising and quantum chemistry problems, reaching systems of up to ten qubits within realistic computational budgets. This framework demonstrates how learned, reusable circuit building blocks can guide the co-design of algorithms and hardware for quantum processors.
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