Machine learning algorithms for many-body quantum systems
Collection of NetKet lectures
VQE simulation of the Kagome antiferromagnet (Mn₃Sn) — HEA & MERA ansatze, entanglement entropy, ED benchmarks, SOC QAOA.
Transcorrelated Second-Quantized Neural Network Quantum States (TC-NQS). A JAX-based framework for high-precision quantum chemistry, utilizing transcorrelation theory and efficient second-order imaginary time evolution solvers (VITE, MinSR, ProjectedSR).
A PyTorch implementation of a Neural Quantum State (NQS) simulator for quantum many-body systems, featuring symmetry-preserving neural networks and advanced sampling techniques.
This project provides a comprehensive framework for simulating quantum systems using various models and functionalities. The project leverages advanced mathematical libraries and parallel computing techniques to ensure efficient and accurate simulations.
Results presented during the EuCAIFCon 2025 in Cagliari (ITA) (https://agenda.infn.it/event/43565/). Preprint available.
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