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.
arXiv:2609.22342v1 Announce Type: cross Abstract: Neural networks provide expressive representations for scientific computing. However, even sufficiently expressive networks can suffer training failu...
arXiv:2607. 02292v1 Announce Type: new Abstract: Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions.
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.
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.
The paper investigates how stochastic reconfiguration (SR), the standard optimizer for neural quantum states (NQS), functions as a statistical filter in overparameterized regimes where parameters outnumber Monte Carlo samples. By interpreting SR as ridge regression on tangent features, the authors show that the diagonal shift balances useful update directions against variance from fitting finite-sample residuals, leading to a U-shaped validation risk curve. They introduce multi-shift SR (MS‑SR), which averages ridge solutions at data‑adaptive shifts, and demonstrate that it reduces validation risk and update variance compared to fixed‑shift SR in both small and large system experiments.
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).
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.
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.
arXiv:2608. 06597v1 Announce Type: cross Abstract: A scientific theory of deep learning, comprising learning dynamics and statistical properties of learned models, is rapidly gaining attention.
arXiv:2508.10228v3 Announce Type: replace Abstract: A local-valley (LV) centered approach to assessing the quality of sampling from Restricted Boltzmann Machines (RBMs) was applied to the latest gene...
arXiv:2607. 20943v1 Announce Type: cross Abstract: Quantum Phase Estimation (QPE) is a foundational algorithm for molecular ground-state energy estimation, but its deep circuit requirements make direct hardware execution impractical on Noisy Intermediate-Scale Quantum (NISQ) devices.
arXiv:2606. 15983v1 Announce Type: cross Abstract: Recent theoretical progress has established conditions under which machine learning models can efficiently predict ground-state properties of gapped local Hamiltonians when trained on quantum-generated data.