论文标题
使用加固学习的量子计算的编译器优化
Compiler Optimization for Quantum Computing Using Reinforcement Learning
论文作者
论文摘要
一旦编码为量子电路，任何量子计算应用程序都必须在量子计算机上执行之前进行编译。与经典汇编类似，量子汇编是一个顺序过程，具有许多汇编步骤，并且可能通过许多可能的优化。尽管有相似之处，但用于量子计算的编译器的发展仍处于起步阶段 - 在最佳传球，兼容性，适应性和灵活性上缺乏相互巩固。在这项工作中，我们利用了数十年的经典编译器优化，并提出了一个增强学习框架，以开发优化的量子电路编译流。通过不同的约束和统一的界面，该框架支持单个编译流中不同编译器和优化工具的技术组合。实验评估表明，提出的框架 - 从IBM的Qiskit和Quantinuum的TKET中选择了一系列汇编，在73％的案件中，关于预期的忠诚度的案例中，两个单独的编译器都显着优于两个单独的编译器。该框架可在GitHub（https://github.com/cda-tum/mqtpredictor）上获得，作为慕尼黑量子工具包（MQT）的一部分。
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).
