mlsanityData to 14 Oct 2026

One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective

arXiv 2607.02292 ↗First posted 2 Jul 2026cs.LG cond-mat.dis-nn quant-phcited by 0

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

Abstract (arXiv)

Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions. Among NQS parameterizations, autoregressive models are especially attractive because they enable exact, independent sampling from the Born distribution, avoiding the autocorrelation and mixing issues of Markov chain methods. Yet their optimization remains comparatively underexplored: Adam is a scalable method but ignores function space geometry, while stochastic reconfiguration is principled but costly and numerically fragile in large models. To address this gap, we show that variational energy minimization can be viewed as an advantage policy-gradient problem over the Born distribution, motivating trust-region optimization for NQS training. We introduce Proximal Wavefunction Optimization (PWO), a principled trust-region algorithm that clips probability-ratio changes in the amplitude channel and phase increments in the phase channel. PWO avoids explicit matrix inversion, reuses samples across multiple updates, and combines the scalability of first-order optimization with theoretical guarantees. Across Ising and frustrated J1-J2 one- and two-dimensional spin systems, PWO improves stability and wall-clock convergence over Adam, minSR, and SPRING. Finally, we fine-tune a 1.5B-parameter RWKV-7 model, demonstrating NQS optimization at a scale over three orders of magnitude beyond prior work.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 16 of its 48 references that are in the map. First posted on arXiv 2 Jul 2026.

Builds on

16 of 48 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Reinforcement learning to learn quantum states for Heisenberg scaling accuracy3 Dec

Other references · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Trust Region Policy Optimizationcited by 2,580
  3. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
  4. 2015Optimizing Neural Networks with Kronecker-factored Approximate Curvaturecited by 796
  5. 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 200
  6. 2025RWKV-7 "Goose" with Expressive Dynamic State Evolutioncited by 141
  7. 2019Deep neural network solution of the electronic Schr"odinger equationcited by 127
  8. 2019Deep autoregressive models for the efficient variational simulation of many-body quantum systemscited by 64
  9. 2022A Self-Attention Ansatz for Ab-initio Quantum Chemistrycited by 49
  10. 2019Efficient Subsampled Gauss-Newton and Natural Gradient Methods for Training Neural Networkscited by 33
  11. 2024A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctionscited by 26
  12. 2025Evolution Strategies at the Hyperscalecited by 25
3 more
  1. 2024Ab-initio variational wave functions for the time-dependent many-electron Schr"odinger equationcited by 7
  2. 2025Generalized Lanczos method for systematic optimization of neural-network quantum statescited by 2
  3. 2026Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlocited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023A Score-Based Model for Learning Neural Wavefunctions
  2. 2026Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoders
  3. 2025Breaking Through Barren Plateaus: Reinforcement Learning Initializations for Deep Variational Quantum Circuits
  4. 2026Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians
  5. 2026Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
  6. 2026Reinforcement Learning for Quantum Technology
  7. 2026Modeling quantum neural network gradient with reinforcement learning
  8. 2021Quantum Optimization for Training Quantum Neural Networks