Compiler Optimization for Quantum Computing Using Reinforcement Learning

Abstract

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).

6 pages, 3 figures, minor changes, to be published at Design Automation Conference (DAC), 2023

References in corpus (8)

• Quantum Circuit Simplification and Level Compaction
• MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing
• Exact synthesis of multiqubit Clifford+T circuits
• Quantum circuit optimization with deep reinforcement learning
• MLGO: a Machine Learning Guided Compiler Optimizations Framework
• Predicting Good Quantum Circuit Compilation Options
• AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
• A SAT Encoding for Optimal Clifford Circuit Synthesis

Cited by in corpus (9)

• The MQT Handbook: A Summary of Design Automation Tools and Software for Quantum Computing
• A Comprehensive Review of Quantum Circuit Optimization: Current Trends and Future Directions
• Artificial Intelligence for Quantum Computing
• Predicting Good Quantum Circuit Compilation Options
• MQT Predictor: Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing
• Crosstalk Attacks and Defence in a Shared Quantum Computing Environment
• A Hybrid Classical Quantum Computing Approach to the Satellite Mission Planning Problem
• Reducing the Compilation Time of Quantum Circuits Using Pre-Compilation on the Gate Level
• Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation