mlsanityData to 14 Oct 2026

Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware

arXiv 2411.00230 ↗First posted 31 Oct 2024quant-ph cs.AI cs.LGcited by 7

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

Quantum computing offers exciting opportunities for simulating complex quantum systems and optimizing large scale combinatorial problems, but its practical use is limited by device noise and constrained connectivity. Designing quantum circuits, which are fundamental to quantum algorithms, is therefore a central challenge in current quantum hardware. Existing reinforcement learning based methods for circuit design lose accuracy when restricted to hardware native gates and device level compilation. Here, we introduce gadget reinforcement learning (GRL), which combines learning with program synthesis to automatically construct composite gates that expand the action space while respecting hardware constraints. We show that this approach improves accuracy, hardware compatibility, and scalability for transverse-field Ising and quantum chemistry problems, reaching systems of up to ten qubits within realistic computational budgets. This framework demonstrates how learned, reusable circuit building blocks can guide the co-design of algorithms and hardware for quantum processors.

Key facts. Cited by 7 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 27 of its 78 references that are in the map. First posted on arXiv 31 Oct 2024.

Builds on

27 of 78 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search7 Oct
  2. 2024Reinforcement Learning for Variational Quantum Circuits Design9 Sep
  3. 2024Reinforcement learning-assisted quantum architecture search for variational quantum algorithms21 Feb
  4. 2024Curriculum reinforcement learning for quantum architecture search under hardware errors5 Feb

Other references · 23

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2018Diversity is All You Need: Learning Skills without a Reward Functioncited by 752
  4. 2018Data-Efficient Hierarchical Reinforcement Learningcited by 455
  5. 2016The Option-Critic Architecturecited by 448
  6. 2016Variational Intrinsic Controlcited by 267
  7. 2020DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learningcited by 161
  8. 2017Stochastic Neural Networks for Hierarchical Reinforcement Learningcited by 124
  9. 2017Meta Learning Shared Hierarchiescited by 93
  10. 2021Reinforcement learning for optimization of variational quantum circuit architecturescited by 77
  11. 2021Temporal Abstraction in Reinforcement Learning with the Successor Representationcited by 54
  12. 2017DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrationscited by 43
11 more
  1. 2024KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Searchcited by 37
  2. 2024Quantum Circuit Optimization with AlphaTensorcited by 36
  3. 2021Quantum Architecture Search via Continual Reinforcement Learningcited by 22
  4. 2021Optimizing Quantum Variational Circuits with Deep Reinforcement Learningcited by 9
  5. 2023Challenges for Reinforcement Learning in Quantum Circuit Designcited by 9
  6. 2022Reinforcement Learning Assisted Recursive QAOAcited by 8
  7. 2021QuantumCircuitOpt: An Open-source Framework for Provably Optimal Quantum Circuit Designcited by 6
  8. 2023Quantum approximate optimization via learning-based adaptive optimizationcited by 4
  9. 2021How Parallel Circuit Execution Can Be Useful for NISQ Computing?cited by 3
  10. 2022An Empirical Review of Optimization Techniques for Quantum Variational Circuitscited by 3
  11. 2023A Case for Synthesis of Recursive Quantum Unitary Programscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025TensorRL-QAS: Reinforcement learning with tensor networks for improved quantum architecture searchcited by 7
  2. 2025Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardwarecited by 2
  3. 2025BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture searchcited by 2
  4. 2025RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulationscited by 1
  5. 2026Replay-buffer engineering for noise-aware quantum circuit optimizationcited by 1
  6. 2026Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithmscited by 0
  7. 2026Energy Accuracy Is Not Enough: A Structure-Aware Benchmark and Evaluation Protocol for Quantum Architecture Searchcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis
  2. 2024Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning
  3. 2025Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
  4. 2025Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations
  5. 2025Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networks
  6. 2026Reinforcement Learning for Quantum Technology
  7. 2024Reinforcement learning-based architecture search for quantum machine learning
  8. 2026Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis