# Quantum Compiling with Reinforcement Learning on a Superconducting Processor

arXiv: https://arxiv.org/abs/2406.12195 · HTML: https://mlsanity.com/paper/2406.12195/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 93 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [Quantum advantage in learning from experiments](https://mlsanity.com/paper/2112.00778/) (2021)
- [Provably efficient machine learning for quantum many-body problems](https://mlsanity.com/paper/2106.12627/) (2021)
- [Reinforcement learning for optimization of variational quantum circuit architectures](https://mlsanity.com/paper/2103.16089/) (2021)
- [Recent Advances for Quantum Neural Networks in Generative Learning](https://mlsanity.com/paper/2206.03066/) (2022)
- [Topological Quantum Compiling with Reinforcement Learning](https://mlsanity.com/paper/2004.04743/) (2020)
- [Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification](https://mlsanity.com/paper/2301.01597/) (2022)
- [Variational quantum compiling with double Q-learning](https://mlsanity.com/paper/2103.11611/) (2021)
- [Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation](https://mlsanity.com/paper/2211.08737/) (2022)
- [Best Approximate Quantum Compiling Problems](https://mlsanity.com/paper/2106.05649/) (2021)
- [Efficient decomposition of unitary matrices in quantum circuit compilers](https://mlsanity.com/paper/2101.02993/) (2021)
- [Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning](https://mlsanity.com/paper/2204.06904/) (2022)
- [Closing the "Quantum Supremacy" Gap: Achieving Real-Time Simulation of a Random Quantum Circuit Using a New Sunway Supercomputer](https://mlsanity.com/paper/2110.14502/) (2021)
- [Arline Benchmarks: Automated Benchmarking Platform for Quantum Compilers](https://mlsanity.com/paper/2202.14025/) (2022)
- [Policy Gradient Approach to Compilation of Variational Quantum Circuits](https://mlsanity.com/paper/2111.10227/) (2021)
- [Quantum compiling with a variational instruction set for accurate and fast quantum computing](https://mlsanity.com/paper/2203.15574/) (2022)
- [A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks](https://mlsanity.com/paper/2102.04518/) (2021)

## Built on by (most cited first, 4 of 4)

- [AlphaCNOT: Learning CNOT Minimization with Model-Based Planning](https://mlsanity.com/paper/2604.13812/) (2026)
- [Quantum Circuit Pre-Synthesis: Learning Local Edits to Reduce T-count](https://mlsanity.com/paper/2601.19738/) (2026)
- [Replay-buffer engineering for noise-aware quantum circuit optimization](https://mlsanity.com/paper/2604.21863/) (2026)
- [AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL](https://mlsanity.com/paper/2608.18946/) (2026)

## Abstract (arXiv)

To effectively implement quantum algorithms on noisy intermediate-scale quantum (NISQ) processors is a central task in modern quantum technology. NISQ processors feature tens to a few hundreds of noisy qubits with limited coherence times and gate operations with errors, so NISQ algorithms naturally require employing circuits of short lengths via quantum compilation. Here, we develop a reinforcement learning (RL)-based quantum compiler for a superconducting processor and demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. We show that for the three-qubit quantum Fourier transformation, a compiled circuit using only seven CZ gates with unity circuit fidelity can be achieved. The compiler is also able to find optimal circuits under device topological constraints, with lengths considerably shorter than those by the conventional method. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation, offering valuable insights for the advancement of RL-based compilers.