# AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search

arXiv: https://arxiv.org/abs/2410.05115 · HTML: https://mlsanity.com/paper/2410.05115/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 20 of its 61 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [Quantum reinforcement learning](https://mlsanity.com/deeprl/quantum-reinforcement-learning/)

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [Meta-Q-Learning](https://mlsanity.com/paper/1910.00125/) (2019)
- [P3O: Policy-on Policy-off Policy Optimization](https://mlsanity.com/paper/1905.01756/) (2019)
- [Continuous Doubly Constrained Batch Reinforcement Learning](https://mlsanity.com/paper/2102.09225/) (2021)
- [Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers](https://mlsanity.com/paper/1901.11054/) (2019)
- [Learning to Solve Vehicle Routing Problems: A Survey](https://mlsanity.com/paper/2205.02453/) (2022)
- [On the qubit routing problem](https://mlsanity.com/paper/1902.08091/) (2019)
- [Compiler Optimization for Quantum Computing Using Reinforcement Learning](https://mlsanity.com/paper/2212.04508/) (2022)
- [Qubit Routing using Graph Neural Network aided Monte Carlo Tree Search](https://mlsanity.com/paper/2104.01992/) (2021)
- [Packet Routing with Graph Attention Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2107.13181/) (2021)
- [Optimality Study of Existing Quantum Computing Layout Synthesis Tools](https://mlsanity.com/paper/2002.09783/) (2020)
- [Optimal qubit assignment and routing via integer programming](https://mlsanity.com/paper/2106.06446/) (2021)
- [Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks](https://mlsanity.com/paper/2012.06701/) (2020)
- [Arline Benchmarks: Automated Benchmarking Platform for Quantum Compilers](https://mlsanity.com/paper/2202.14025/) (2022)
- [Budgeting Counterfactual for Offline RL](https://mlsanity.com/paper/2307.06328/) (2023)

## Built on by (most cited first, 8 of 8)

- [Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware](https://mlsanity.com/paper/2411.00230/) (2024)
- [Quantum computing and artificial intelligence: status and perspectives](https://mlsanity.com/paper/2505.23860/) (2025)
- [BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search](https://mlsanity.com/paper/2507.12189/) (2025)
- [Quantum-Efficient Reinforcement Learning Solutions for Last-Mile On-Demand Delivery](https://mlsanity.com/paper/2508.09183/) (2025)
- [Shuttling Compiler for Trapped-Ion Quantum Computers Based on Fine-Tuned Large Language Models](https://mlsanity.com/paper/2512.18021/) (2025)
- [Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passes](https://mlsanity.com/paper/2601.21629/) (2026)
- [QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning](https://mlsanity.com/paper/2605.12365/) (2026)
- [Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization](https://mlsanity.com/paper/2607.29145/) (2026)

## Abstract (arXiv)

Quantum computers have the potential to outperform classical computers in important tasks such as optimization and number factoring. They are characterized by limited connectivity, which necessitates the routing of their computational bits, known as qubits, to specific locations during program execution to carry out quantum operations. Traditionally, the NP-hard optimization problem of minimizing the routing overhead has been addressed through sub-optimal rule-based routing techniques with inherent human biases embedded within the cost function design. This paper introduces a solution that integrates Monte Carlo Tree Search (MCTS) with Reinforcement Learning (RL). Our RL-based router, called AlphaRouter, outperforms the current state-of-the-art routing methods and generates quantum programs with up to 20% less routing overhead, thus significantly enhancing the overall efficiency and feasibility of quantum computing.