Neural Network Solves Quantum Few-Body Systems
Paolo Recchia submitted an arXiv preprint on Mar 13, 2026 presenting a neural-network framework to solve quantum few-body systems, including identical and nonidentical particles. The model combines adaptive step sizes with Metropolis-Adjusted Langevin Algorithm sampling and GPU acceleration to approximate ground-state wavefunctions, handling harmonic confinement, Gaussian two-body interactions, and three-body forces while outperforming prior ML methods on ten-particle benchmarks. This approach scales favorably, reduces hyperparameter sensitivity, and captures spatial correlations.
Scoring Rationale
Strong methodological advance with lower errors and demonstrable scalability, limited by a single-source arXiv preprint without peer-reviewed validation.
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