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Fermionic wave functions from neural-network constrained hidden states

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Hamiltonian reconstruction as metric for variational studies

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Variational approaches are among the most powerful techniques to approximately solve quantum many-body problems. These encompass both variational states based on tensor or neural networks, and parameterized quantum circuits in variational quantum eigensolv ...

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Koopmans spectral functionals aim to describe simultaneously ground-state properties and charged excitations of atoms, molecules, nanostructures, and periodic crystals. This is achieved by augmenting standard density functionals with simple but physically ...

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We introduce HP, an implementation of density-functional perturbation theory, designed to compute Hubbard parameters (on-site U and inter-site V ) in the framework of DFT+U and DFT+U+V. The code does not require the use of computationally expensive superce ...

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Revealing higher-order light and matter energy exchanges using quantum trajectories in ultrastrong coupling

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The dynamics of open quantum systems is often modeled using master equations, which describe the expected outcome of an experiment (i.e., the average over many realizations of the same dynamics). Quantum trajectories, instead, model the outcome of ideal si ...

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Multi-layer State Evolution Under Random Convolutional Design

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Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challenging. For generati ...

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Spinor GW /Bethe-Salpeter calculations in BerkeleyGW: Implementation, symmetries, benchmarking, and performance

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Computing the GW quasiparticle band structure and Bethe-Salpeter equation (BSE) absorption spectra for materials with spin-orbit coupling have commonly been done by treating GW corrections and spin-orbit coupling (SOC) as separate perturbations to density- ...

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Dynamical Control of Nuclear Isomer Depletion via Electron Vortex Beams

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Some nuclear isomers are known to store a large amount of energy over long periods of time, with a very high energy-to-mass ratio. Here, we describe a protocol to achieve the external control of the isomeric nuclear decay by using electron vortex beams who ...

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During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...

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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 ...

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Visualizing the multifractal wave functions of a disordered two-dimensional electron gas

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The wave functions of a disordered two-dimensional electron gas at the quantum-critical Anderson transition are predicted to exhibit multifractal scaling in their real space amplitude. We experimentally investigate the appearance of these characteristics i ...

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Hidden-nucleons neural-network quantum states for the nuclear many-body problem

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We generalize the hidden-fermion family of neural network quantum states to encompass both continuous and discrete degrees of freedom and solve the nuclear many-body Schrodinger equation in a systematically improvable fashion. We demonstrate that adding hi ...

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Charge ordering in Ir dimers in the ground state of Ba5AlIr2O11

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It has been well established experimentally that the interplay of electronic correlations and spin-orbit interactions in Ir4+ and Ir5+ oxides results in insulating J(eff) = 1/2 and J(eff) = 0 ground states, respectively. However, in compounds where the str ...

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A unified framework for Hamiltonian deep neural networks

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Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/explod- ing gradients during weight optimization. To avoid this problem, we propose a class of DNNs stemming from the time discretization of Hamiltonian systems. The t ...

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Biologically plausible unsupervised learning in shallow and deep neural networks

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The way our brain learns to disentangle complex signals into unambiguous concepts is fascinating but remains largely unknown. There is evidence, however, that hierarchical neural representations play a key role in the cortex. This thesis investigates biolo ...

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Leveraging topology, geometry, and symmetries for efficient Machine Learning

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When learning from data, leveraging the symmetries of the domain the data lies on is a principled way to combat the curse of dimensionality: it constrains the set of functions to learn from. It is more data efficient than augmentation and gives a generaliz ...

EPFL2022