Publications de Dian Wu | EPFL Graph Search

Learning eigenstates of quantum many-body Hamiltonians within the symmetric subspaces using neural network quantum states

The exploration of neural network quantum states has become widespread in studies of complicated quantum many-body systems. However, achieving high precision remains challenging due to the exponential growth of Hilbert space size and the intricate sign str ...

2025

Variational benchmarks for quantum many-body problems

Andreas Martin Läuchli Herzig, Giuseppe Carleo, Dian Wu, Filippo Vicentini, Riccardo Rossi, Jannes Willy E. Nys

The continued development of computational approaches to many-body ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. In this work, we introduce a metric of variational accuracy, the V-score, obtained ...

American Association for the Advancement of Science2024

Sparse autoregressive neural networks for classical spin systems

Giuseppe Carleo, Dian Wu, Indaco Biazzo

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Iop Publishing Ltd2024

Exact Sampling for Classical and Quantum Many-Body Systems

Dian Wu

Many-body systems at low temperature have revealed non-trivial phases of materials, such as spin liquids, which have found applications in the evolving fields of superconductivity, nanoelectronics, and quantum computing. Their exponentially large state spa ...

EPFL2024

From tensor-network quantum states to tensorial recurrent neural networks

Giuseppe Carleo, Dian Wu, Filippo Vicentini, Riccardo Rossi

We show that any matrix product state (MPS) can be exactly represented by a recurrent neural network (RNN) with a linear memory update. We generalize this RNN architecture to two-dimensional lattices using a multilinear memory update. It supports perfect s ...

AMER PHYSICAL SOC2023

RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior

Dian Wu

Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG ...

IOP Publishing Ltd2022

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Giuseppe Carleo, Dian Wu, Riccardo Rossi

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to s ...

AMER PHYSICAL SOC2021