Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer. Similar to classical compilation, quantum compilation is a sequential process with many compilation steps and numerous possible optimization passes. Despite the similarities, the development of compilers for quantum computing is still in its infancy -- lacking mutual consolidation on the best sequence of passes, compatibility, adaptability, and flexibility. In this work, we take advantage of decades of classical compiler optimization and propose a reinforcement learning framework for developing optimized quantum circuit compilation flows. Through distinct constraints and a unifying interface, the framework supports the combination of techniques from different compilers and optimization tools in a single compilation flow. Experimental evaluations show that the proposed framework -- set up with a selection of compilation passes from IBM's Qiskit and Quantinuum's TKET -- significantly outperforms both individual compilers in 73% of cases regarding the expected fidelity. The framework is available on GitHub (https://github.com/cda-tum/MQTPredictor) as part of the Munich Quantum Toolkit (MQT).

翻译：任何量子计算应用，一旦被编码为量子电路，就必须经过编译才能在执行。与经典编译类似，量子编译也是一个包含众多编译步骤和大量优化通路的顺序过程。尽管存在相似性，量子计算编译器的开发仍处于初期阶段——在最优通路序列、兼容性、适应性和灵活性方面缺乏共识。本文借鉴了数十年来经典编译器优化的经验，提出了一种基于强化学习的框架，用于开发优化的量子电路编译流程。该框架通过独特的约束条件和统一接口，支持在单个编译流程中整合来自不同编译器和优化工具的技术。实验评估表明，该框架——结合了IBM Qiskit和Quantinuum TKET中的一系列编译通路——在73%的情况下在预期保真度上显著优于各单独编译器。该框架作为慕尼黑量子工具包(MQT)的一部分，可在GitHub上获取(https://github.com/cda-tum/MQTPredictor)。