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：任何量子计算应用程序，一旦编码成量子电路，必须在在量子计算机上可执行之前进行编译。类似于经典编译，量子编译是一个顺序过程，有许多编译步骤和众多可能的优化通路。尽管相似，量子计算编译器的开发仍处于其萌芽阶段--缺乏最佳通路的共识、兼容性、适应性和灵活性。在这项工作中，我们利用几十年的经典编译器优化，并提出了一种强化学习框架，用于开发优化的量子电路编译流程。通过不同的约束和一个统一的接口，该框架支持从不同的编译器和优化工具中结合技术在单个编译流程中。实验评估表明，所提出的框架--使用IBM的Qiskit和Quantinuum的TKET的选定编译通路设置--在预期保真度方面在73%的情况下显着优于单个编译器。该框架作为慕尼黑量子工具包（MQT）的一部分已在GitHub（https://github.com/cda-tum/MQTPredictor）上提供。