# Compiler Optimization for Quantum Computing Using Reinforcement Learning

arXiv: https://arxiv.org/abs/2212.04508 · HTML: https://mlsanity.com/paper/2212.04508/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [A Survey on Compiler Autotuning using Machine Learning](https://mlsanity.com/paper/1801.04405/) (2018)
- [MLGO: a Machine Learning Guided Compiler Optimizations Framework](https://mlsanity.com/paper/2101.04808/) (2021)
- [NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning](https://mlsanity.com/paper/1909.13639/) (2019)
- [AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.00671/) (2020)
- [SupermarQ: A Scalable Quantum Benchmark Suite](https://mlsanity.com/paper/2202.11045/) (2022)

## Built on by (most cited first, 11 of 11)

- [Quantum Circuit Optimization with AlphaTensor](https://mlsanity.com/paper/2402.14396/) (2024)
- [How Far Are We From AGI: Are LLMs All We Need?](https://mlsanity.com/paper/2405.10313/) (2024)
- [Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning](https://mlsanity.com/paper/2405.13196/) (2024)
- [AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search](https://mlsanity.com/paper/2410.05115/) (2024)
- [Improving Quantum Circuit Synthesis with Machine Learning](https://mlsanity.com/paper/2306.05622/) (2023)
- [Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation](https://mlsanity.com/paper/2405.08100/) (2024)
- [Quantum Artificial Intelligence: A Brief Survey](https://mlsanity.com/paper/2408.10726/) (2024)
- [Quantum Circuit Pre-Synthesis: Learning Local Edits to Reduce T-count](https://mlsanity.com/paper/2601.19738/) (2026)
- [Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passes](https://mlsanity.com/paper/2601.21629/) (2026)
- [Family-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance](https://mlsanity.com/paper/2606.11620/) (2026)
- [AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization](https://mlsanity.com/paper/2608.12936/) (2026)

## Abstract (arXiv)

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).