Machine learning algorithms for many-body quantum systems
VQE simulation of the Kagome antiferromagnet (Mn₃Sn) — HEA & MERA ansatze, entanglement entropy, ED benchmarks, SOC QAOA.
Collection of NetKet lectures
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.
A PyTorch implementation of a Neural Quantum State (NQS) simulator for quantum many-body systems, featuring symmetry-preserving neural networks and advanced sampling techniques.
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).
Results presented during the EuCAIFCon 2025 in Cagliari (ITA) (https://agenda.infn.it/event/43565/). Preprint available.
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