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physics.comp-ph2026
Advancing Machine Learning Applications in Quantum Few-Body Systems
Jin Ziqi, Paolo Recchia, Mario Gattobigio
This paper presents a general neural network framework for solving quantum few-body systems, extending prior methods to handle diverse particle masses, interaction types, and syste…
physics.comp-ph2024
The Steepest Slope toward a Quantum Few-body Solution: Gradient Variational Methods for the Quantum Few-body Problem
Paolo Recchia, Debabrota Basu, Mario Gattobigio +2
Quantum few-body systems are deceptively simple. Indeed, with the notable exception of a few special cases, their associated Schrodinger equation cannot be solved analytically for…