mlsanityData to 14 Oct 2026

Quantum Compiling with Reinforcement Learning on a Superconducting Processor

arXiv 2406.12195 ↗First posted 18 Jun 2024quant-ph cs.LGcited by 4

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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.

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

24 of 93 references.

References · 24

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12 more
  1. 2020Topological Quantum Compiling with Reinforcement Learningcited by 23
  2. 2022Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classificationcited by 20
  3. 2021Variational quantum compiling with double Q-learningcited by 13
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  8. 2021Closing the "Quantum Supremacy" Gap: Achieving Real-Time Simulation of a Random Quantum Circuit Using a New Sunway Supercomputercited by 5
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026AlphaCNOT: Learning CNOT Minimization with Model-Based Planningcited by 2
  2. 2026Quantum Circuit Pre-Synthesis: Learning Local Edits to Reduce T-countcited by 1
  3. 2026Replay-buffer engineering for noise-aware quantum circuit optimizationcited by 1
  4. 2026AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RLcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026CO-MAP: A Reinforcement Learning Approach to the Qubit Allocation Problem
  2. 2025Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
  3. 2024Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning
  4. 2024A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis
  5. 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
  6. 2025Pauli Network Circuit Synthesis with Reinforcement Learning
  7. 2026Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis
  8. 2025Separating Ansatz Discovery from Deployment on Larger Problems: Reinforcement Learning for Modular Circuit Design