Publications de Dian Wu | EPFL Graph Search

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Dian Wu

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Unbiased Monte Carlo cluster updates with autoregressive neural networks

Exact Sampling for Classical and Quantum Many-Body Systems

Sparse autoregressive neural networks for classical spin systems

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

From tensor-network quantum states to tensorial recurrent neural networks

Variational benchmarks for quantum many-body problems

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