mlsanityData to 14 Oct 2026

Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardware

arXiv 2501.11454 ↗First posted 20 Jan 2025quant-ph cs.AI cs.LGcited by 2

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

Abstract (arXiv)

The Sachdev-Ye-Kitaev (SYK) model, known for its strong quantum correlations and chaotic behavior, serves as a key platform for quantum gravity studies. However, variationally preparing thermal states on near-term quantum processors for large systems (N>12, where N is the number of Majorana fermions) presents a significant challenge due to the rapid growth in the complexity of parameterized quantum circuits. This paper addresses this challenge by integrating reinforcement learning (RL) with convolutional neural networks, employing an iterative approach to optimize the quantum circuit and its parameters. The refinement process is guided by a composite reward signal derived from entropy and the expectation values of the SYK Hamiltonian. This approach reduces the number of CNOT gates by two orders of magnitude for systems N≥12 compared to traditional methods like first-order Trotterization. We demonstrate the effectiveness of the RL framework in both noiseless and noisy quantum hardware environments, maintaining high accuracy in thermal state preparation. This work advances a scalable, RL-based framework with applications for quantum gravity studies and out-of-time-ordered thermal correlators computation in quantum many-body systems on near-term quantum hardware. The code is available at https://github.com/Aqasch/solvingSYKmodelwithRL.

Key facts. Cited by 2 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 17 of its 79 references that are in the map. First posted on arXiv 20 Jan 2025.

Builds on

17 of 79 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware31 Oct
  2. 2024Reinforcement learning-assisted quantum architecture search for variational quantum algorithms21 Feb
  3. 2024Curriculum reinforcement learning for quantum architecture search under hardware errors5 Feb

Other references · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2020Variational Quantum Algorithmscited by 692
  4. 2018A Survey on Deep Transfer Learningcited by 419
  5. 2021Reinforcement learning for optimization of variational quantum circuit architecturescited by 77
  6. 2021Quantum Architecture Search via Deep Reinforcement Learningcited by 60
  7. 2024KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Searchcited by 37
  8. 2019Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithmcited by 27
  9. 2014Flattened Convolutional Neural Networks for Feedforward Accelerationcited by 20
  10. 2016Good Practice in CNN Feature Transfercited by 12
  11. 2023Enhancing variational quantum state diagonalization using reinforcement learning techniquescited by 11
  12. 2022Reinforcement Learning Assisted Recursive QAOAcited by 8
2 more
  1. 2021Entanglement Diagnostics for Efficient Quantum Computationcited by 2
  2. 2024Artificially intelligent Maxwell's demon for optimal control of open quantum systemscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture searchcited by 2
  2. 2026AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RLcited by 0

Similar, not cited

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  3. 2025Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
  4. 2025TensorRL-QAS: Reinforcement learning with tensor networks for improved quantum architecture search
  5. 2024A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis
  6. 2025Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing
  7. 2026Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
  8. 2025Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations