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AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search
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AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search
abstract
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.
Forward citations
Cited by 5 Pith papers
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Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization
A learning-to-rank model over feature-model-sampled Qiskit transpiler pass configurations reliably outperforms Qiskit's fixed optimization levels on two-qubit gate reduction.
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Shielded RL for Route-Charged Parity-Term Ordering in QEDA Phase Components
Shielded RL reordering of commuting phase terms cuts routed CNOT counts by 5.7–12.2% over search baselines on parity-walk QEDA components, but the proxy does not transfer to extraction-heavy or token/permutation circuits.
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MLIR for Quantum Beyond Gate Cancellation: Quantum Circuit Mapping Reimagined
An MLIR-native A* qubit-routing pass outperforms QMAP and TKET on SWAP count and runtime and integrates into an open MLIR quantum compiler.
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Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing
A calibration-aware graph RL router achieves pooled mean fidelity of 0.727 on nine MQT Bench circuits across three IBM calibration snapshots, outperforming SABRE-best20 (0.440) and target-aware SABRE (0.481).
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Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing
A calibration-aware graph reinforcement-learning router improves exact simulated fidelity by ~0.25-0.29 over SABRE baselines on 5-8 qubit MQT Bench circuits, while 10-qubit circuits still favor SABRE.
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