CORTEXA
← Browse
arxivphysics.chem-phcond-mat.stat-mechcs.LGphysics.comp-phquant-ph2026-07-22

Nuclear Quantum Effects as a Denoising Problem

Weizhou Wang, Jonathan Weare, Aaron R. Dinner

Nuclear quantum effects are rigorously captured by imaginary-time path integrals, which map the quantum Boltzmann distribution onto a ring polymer of classical replicas. Yet the nuclear masses, the coupling to the environment, and the boundary conditions of the path remain hard-wired in the simulation or the trained model, even though this quantum context enters the path measure only through a quadratic action known in closed form. Here we show that a denoiser trained on classical Boltzmann statistics alone, composed at sampling time with an analytic Gaussian component carrying the entire quantum context, yields the quantum Boltzmann distribution of the nuclei. Such a composition exists and is exact whenever the training noise does not exceed the intrinsic quantum uncertainty of the target ensemble, and it is invariant across all quantum contexts admitted by this bound. We show exact transfer across temperature, isotopic mass, dissipation strength, and the boundary conditions of the path in theory and in numerical experiments, without retraining. The last yields the end-to-end displacement and momentum distributions of a tagged nucleus from open imaginary-time paths. The same invariance extends in principle to the permuted boundary conditions of bosonic exchange, with the identical denoiser. In this view, the noise of generative modeling and the quantum fluctuations of the nuclei are two faces of the same quadratic structure.

View free PDFSource page

Related papers

arxivquant-phcs.LGphysics.chem-phphysics.comp-ph2026-07-21

Enhanced Neural Quantum State via Annealed Gradient Descent

Shiwei Zhou, Yiming Huang, Xiao Yuan, Xiaoxia Cai

Neural quantum states offer expressive representations of quantum many-body wave functions, yet their practical accuracy can be limited by stochastic optimization rather than representational capacity. Here we identify a finite-sample instability, termed subspace trapping, in whi…

View free PDFSource page
arxivcs.LGphysics.chem-phphysics.comp-phquant-ph2026-07-19

Grounded verification of chemical and materials reasoning: detection is the bottleneck

Can Polat, Mustafa Kurban, Erchin Serpedin, Hasan Kurban

Large language models confabulate chemical objects (molecular formulas, space groups, formation energies) in fluent reasoning traces, concentrated on long-tail entities where confidence is least trustworthy. Deterministic, database-grounded verification can catch and repair such…

View free PDFSource page
arxivquant-phcs.LGphysics.chem-ph2026-06-29

Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations

Anurag K. S. V., Ashish Kumar Patra, Manas Mukherjee, Ruchika Bhat, Sai Shankar P., Rahul Maitra, et al.

Calculation of binding energies for protein-ligand molecular systems requires accurate treatment of the electronic structure, a quantum chemistry problem that scales exponentially on classical hardware, while current quantum hardware remains too noisy for the required circuit dep…

View free PDFSource page
arxivphysics.chem-phcs.LGquant-ph2026-06-30

Position: The Inevitable Transition to Machine Learning in Quantum Chemistry

Karen Sargsyan, Chao-Ping Hsu

Finding exact solutions to the quantum many-body problem is computationally intractable (QMA-hard). Traditional approximations for electrons in an atom or molecule -- density functional theory and wavefunction methods -- have been indispensable, but their development shows signs…

View free PDFSource page
arxivcs.LGmath.NAphysics.chem-phphysics.comp-phphysics.data-an2026-07-08

Higher-Order Geometric Updates for Levenberg-Marquardt Method via Riemann Normal Coordinates

Jianing Liu, Dong H. Zhang

Nonlinear least-squares optimization is central to regression, physics-informed neural networks, and other machine-learning tasks. Such problems have a natural geometric interpretation, model predictions form a manifold in data space, while the chosen parameterization can introdu…

View free PDFSource page
arxivquant-phcs.ETcs.LGphysics.chem-ph2026-07-23

An Analytically Trained Variational Surrogate for Quantum Phase Estimation on NISQ Hardware

Mousumi Kundu, Ashish Kumar Patra, Anurag K. S. V., Ruchika Bhat, Sai Shankar P., Alok Shukla, et al.

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. We present an analytically grounded variationa…

View free PDFSource page