用AI智能调度三类优化器，提升量子电路在实际硬件上的运行效率
OrQstrator: An AI-Powered Framework for Advanced Quantum Circuit Optimization
- 通过深度强化学习协调三个互补优化模块
- 在真实量子硬件上实现更低门数和更短深度的电路
- 适合量子算法开发者与硬件适配研究者使用
我们提出一种名为OrQstrator的新框架，用于在噪声中等规模量子（NISQ）时代进行量子电路优化。该框架基于深度强化学习（DRL），由一个中央编排引擎协调三个互补优化模块：基于DRL的电路重写器，通过学习重写序列减少电路深度和门数；领域特定优化器，执行高效的局部门重构与数值优化；参数化电路实例化器，在门集转换过程中优化模板电路。编排引擎根据电路结构、硬件约束及后端感知性能特征（如门数、深度、预期保真度）学习协调策略，输出适配硬件的优化电路。系统借助现有先进方法NISQ Analyzer的技术，动态适应后端约束，实现硬件感知的编译与执行。
原文摘要 · Abstract (English)
We propose a novel approach, OrQstrator, which is a modular framework for conducting quantum circuit optimization in the Noisy Intermediate-Scale Quantum (NISQ) era. Our framework is powered by Deep Reinforcement Learning (DRL). Our orchestration engine intelligently selects among three complementary circuit optimizers: A DRL-based circuit rewriter trained to reduce depth and gate count via learned rewrite sequences; a domain-specific optimizer that performs efficient local gate resynthesis and numeric optimization; a parameterized circuit instantiator that improves compilation by optimizing template circuits during gate set translation. These modules are coordinated by a central orchestration engine that learns coordination policies based on circuit structure, hardware constraints, and backend-aware performance features such as gate count, depth, and expected fidelity. The system outputs an optimized circuit for hardware-aware transpilation and execution, leveraging techniques from an existing state-of-the-art approach, called the NISQ Analyzer, to adapt to backend constraints.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。