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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/223230
Neural Quantum States: Fermions on D-Dimensions
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In this work, we explore the use of Neural Quantum States to approximate the ground-state wavefunctions of fully polarized fermionic systems confined in a D-dimensional harmonic trap. Building on the architecture introduced
in [1], we generalize the input representation and network structure to handle arbitrary spatial dimensionality, extending the applicability of the method beyond one-dimensional systems. The antisymmetric nature of the fermionic wavefunction is preserved through the use of equivariant neural layers, and a generalized Slater determinant is constructed from learned single-particle orbitals modulated by a Gaussian envelope. Training is carried out in two stages: first, a supervised pretraining phase based on analytical solutions of the non-interacting system, which is then followed by variational Monte Carlo optimization of the network parameters using the energy as the loss function.
We validate our approach on non-interacting systems with up to 4 particles in 2D and 3 particles in 3D, where analytical solutions are available for benchmarking.
Results show excellent agreement in terms of mean energy, one-body density, and the one-body density matrix, with observed spatial symmetries and degeneracy patterns matching theoretical expectations. While the training
protocol has been generalized to incorporate finite-range interactions, this study focuses on non-interacting systems to establish a solid baseline. The framework developed here provides a flexible and scalable foundation for future exploration of interacting quantum systems in higher dimensions using neural variational methods.
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Màster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2024-2025. Tutors: Arnau Rios Huguet, Javier Rozalén Sarmiento
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CARRASCO ARANGO, Miguel. Neural Quantum States: Fermions on D-Dimensions. [consulta: 21 de gener de 2026]. [Disponible a: https://hdl.handle.net/2445/223230]