arXiv Machine Learning

Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States

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 Machine Learning

Jun 15

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

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.

By Yi-Ran Xue, Rui Wang, Baigeng Wang, Chenan Wei

arXiv Machine Learning

Aug 19

SPSA Hyperparameter Tuning for Variational Quantum Natural Language Inference

The paper investigates how to tune the hyperparameters of simultaneous perturbation stochastic approximation (SPSA) for training a 6‑qubit, 60‑parameter variational quantum natural language inference (QNLI) classifier. By exploring a broad grid of perturbation scales, learning rates, and gain‑decay schedules, the authors found that an AdamW‑style SPSA configuration (c₀=0.01, η=0.10, γ=0.10) achieved 55 % ± 11 % test accuracy, improving over the default SPSA but still 16–19 percentage points below parameter‑shift baselines. Classical‑gain SPSA and Bures‑preconditioned SPSA performed worse, with accuracies of 51 % and 46 % respectively, indicating that two‑sample SPSA gradients suffer from high variance when optimizing many parameters over limited epochs.

By Nayan D'Souza, Christopher J. Agostino

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

arXiv Machine Learning

Jul 27

Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support

arXiv:2607. 22516v1 Announce Type: cross Abstract: A central design principle in modern machine learning and artificial intelligence is to align a model's inductive bias with the structure of its input data.

By Peiyong Wang, Udaya Parampalli, Casey R. Myers

benchmarks safety

arXiv Machine Learning

Jun 24

Quantum Adaptive Self-Attention for Quantum Transformer Models

arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it.

By Chi-Sheng Chen, En-Jui Kuo

llms benchmarks

arXiv Machine Learning

Jun 30

Active Quantum Kernel Acquisition for Gaussian Process Regression

arXiv:2606. 28833v1 Announce Type: new Abstract: Quantum kernel estimation on near-term hardware is shot-budgeted: every entry of the kernel Gram matrix is a Bernoulli expectation that must be sampled with a finite number of circuit executions.

By Jian Xu, Delu Zeng, Qibin Zhao

benchmarks

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

Aug 21

Empirical Characterization of Learning Geometry in Hybrid Quantum Forecasting Models

arXiv:2608. 19497v1 Announce Type: new Abstract: We characterize the learning dynamics of a compact hybrid quantum forecasting model through comparison with a structurally aligned classical baseline.

By Sandra Leticia Ju\'arez-Osorio, Jorge I. Hernandez-Martinez, Jesus Ivan Ruiz-Martinez, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello

benchmarks safety

arXiv AI

Aug 10

Investigating Quantum-Embedded Transformers on Classical Datasets for Cross-Modality Classification

arXiv:2608. 06846v1 Announce Type: cross Abstract: We test whether a parameterized quantum circuit (PQC) improves a hybrid quantum-classical model's performance on classical datasets, using an interface-matched classical map as the control while holding all other components fixed.

By Hao-Yuan Chen

llms

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

Aug 21

Quantum Gaussian processes for prediction of channel observations

arXiv:2608. 19306v1 Announce Type: cross Abstract: Given a set of input states, we consider the task of predicting the expectation value of a Pauli observable at the output of an unknown quantum evolution, using only a limited number of measurements.

By Jonas J\"ager, Yaroslav Khmelnitskiy, Paolo Braccia, Artur Miroszewski, Diego Garc\'ia-Mart\'in, M. Cerezo, Piotr Czarnik

safety

arXiv AI

Jun 2

Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework

arXiv:2606. 01291v1 Announce Type: cross Abstract: Training Variational Quantum Circuits (VQCs) under Noisy Intermediate-Scale Quantum (NISQ) constraints introduces severe computational limitations: classical statevector simulation memory scales exponentially ($\mathcal{O}(2^n)$), and global cost functions suffer from barren plateaus where gradient variance decays exponentially ($\mathcal{O}(1/2^n)$).

By Syed Farhan Ahmad, Gregory T. Byrd

benchmarks

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