标题： OrQstrator：一种用于高级量子电路优化的AI驱动框架 显示英文标题

标题： OrQstrator: An AI-Powered Framework for Advanced Quantum Circuit Optimization

摘要： 我们提出了一种新方法，OrQstrator，这是一个模块化框架，用于在噪声中等规模量子（NISQ）时代进行量子电路优化。 我们的框架由深度强化学习（DRL）驱动。 我们的编排引擎智能地在三种互补的电路优化器之间进行选择：一个基于DRL的电路重写器，通过学习的重写序列来减少深度和门的数量；一个领域特定的优化器，执行高效的局部门重新合成和数值优化；一个参数化的电路实例生成器，在门集转换期间通过优化模板电路来提高编译效果。 这些模块由一个中央编排引擎协调，该引擎根据电路结构、硬件约束和后端感知性能特征（如门数量、深度和预期保真度）学习协调策略。 该系统输出一个针对硬件感知的编译和执行优化后的电路，利用现有最先进的方法NISQ Analyzer的技术来适应后端约束。 显示英文摘要

摘要： 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.