Publications by Jannes Willy E. Nys

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Solving the time-dependent quantum many-body Schrödinger equation is a challenging task, especially for states at a finite temperature, where the environment affects the dynamics. Most existing approximating methods are designed to represent static thermal ...

Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic superfluid Bose-Einstein condensate (BEC). While these properties can be p ...

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

Realand imaginary -time quantum state evolutions are crucial in physics and chemistry for exploring quantum dynamics, preparing ground states, and computing thermodynamic observables. On near -term devices, variational quantum time evolution is a promising ...

Local Hamiltonians of fermionic systems on a lattice can be mapped onto local qubit Hamiltonians. Maintaining the lo-cality of the operators comes at the ex-pense of increasing the Hilbert space with auxiliary degrees of freedom. In order to retrieve the l ...

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 ...

We introduce a message-passing neural-network (NN)-based wave function Ansatz to simulate extended, strongly interacting fermions in continuous space. Symmetry constraints, such as continuous translation symmetries, can be readily embedded in the model. We ...

Understanding the real-time evolution of many-electron quantum systems is essential for studying dynamical properties in condensed matter, quantum chemistry, and complex materials, yet it poses a significant theoretical and computational challenge. Our wor ...

Predicting the dynamical properties of topological matter is a challenging task, not only in theoretical and experimental settings, but also computationally. Numerical studies are often constrained to studying simplified models and lattices. Here we propos ...

We perform the first amplitude analysis of experimental data using deep neural networks to determine the nature of an exotic hadron. Specifically, we study the line shape of the P-c(4312) signal reported by the LHCb collaboration, and we find that its most ...

We show that a neural network trained with synthetic differential intensities calculated with scattering length approximated amplitudes classifies the Pc(4312)+ signal as a virtual state located at the 4th Riemann sheet with very high certainty. This is in ...

Through the introduction of auxiliary fermions, or an enlarged spin space, one can map local fermion Hamiltonians onto local spin Hamiltonians, at the expense of introducing a set of additional constraints. We present a variational MonteCarlo framework to ...

Jannes Willy E. Nys

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Real-time quantum dynamics of thermal states with neural thermofields

Neural-network quantum states for ultra-cold Fermi gases

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Variational quantum time evolution without the quantum geometric tensor

Quantum circuits for solving local fermion-to-qubit map- pings

Variational benchmarks for quantum many-body problems

Message-passing neural quantum states for the homogeneous electron gas

Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation

Predicting topological entanglement entropy in a Rydberg analogue simulator

Phase Diagram and Crystal Melting of Helium-4 in Two Dimensions

Deep learning exotic hadrons

Machine learning exotic hadrons

Variational solutions to fermion-to-qubit mappings in two spatial dimensions