arXiv Machine Learning June 15, 2026 By Yi-Ran Xue, Rui Wang, Baigeng Wang, Chenan Wei

Direct/adaptive-mixture phase-gradient learning for neural-network quantum states with complex phase structure

Read the original on arXiv Machine Learning →

arXiv:2606. 13912v1 Announce Type: cross Abstract: Neural-network quantum states (NQS) are a leading variational tool for quantum many-body physics, yet their optimization is fragile whenever the ground state carries a non-trivial sign or complex phase structure, a situation generic to gauge fields, broken time-reversal symmetry, and fermionic statistics.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 3

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

arXiv:2607. 02292v1 Announce Type: new Abstract: Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions.

By Juan Agust\'in Duque, Sergio Garc\'ia Heredia, Vinicius Hernandes, Eli\v{s}ka Greplov\'a, Thomas Spriggs, Aaron Courville, Anna Dawid

reinforcement-learning fine-tuning

Hugging Face Trending Papers

Jul 2

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

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.

reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 9

Adaptive directional gradients for parameterised quantum circuits

arXiv:2606. 09734v1 Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale.

By Brian Coyle, Snehal Raj, Virag Umathe, El Amine Cherrat, Elham Kashefi

arXiv Machine Learning

Jul 3

Ravines in quantum cost landscapes: opportunities for improved VQA predictions

arXiv:2607. 01329v1 Announce Type: cross Abstract: The geometric and topological structure of quantum cost landscapes (QCLs) governs the optimization and thus the predictive power of variational quantum algorithms (VQAs).

By Felix J. Beckmann, Jo\~ao F. Bravo

arXiv Machine Learning

Jun 19

Entropy Estimation in Multi-Qutrit Systems via Variational and Classical Neural Networks

arXiv:2606. 20504v1 Announce Type: cross Abstract: We present a systematic study of von Neumann entropy estimation in multi-qutrit quantum systems using two complementary approaches: variational quantum algorithms (VQAs) and classical convolutional neural networks (CNNs), evaluated using an ideal (noise-free) quantum simulator.

By Sai Sakunthala Guddanti, Anil Prabhakar, Ria Rushin Joseph

arXiv Machine Learning

Jun 4

Neural Autoregressive Control Variates for the Quantum Monte Carlo Sign Problem

arXiv:2605. 26814v2 Announce Type: replace-cross Abstract: We train a pair of autoregressive models to construct zero-mean control variates to mitigate the sign problem in quantum Monte Carlo simulations.

By Bei Qiao, Lei Wang

llms benchmarks

Related stories

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

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

Adaptive directional gradients for parameterised quantum circuits

Ravines in quantum cost landscapes: opportunities for improved VQA predictions

Entropy Estimation in Multi-Qutrit Systems via Variational and Classical Neural Networks

Neural Autoregressive Control Variates for the Quantum Monte Carlo Sign Problem