We had a very productive Computational Quantum Science Lab group retreat last week, enjoying the beauty of southern Italy (Greek temples, pizzas, and buffaloes included) and discussing quantum physics, NetKet and more!

Had a great time connecting with everyone in the growing Computational Quantum Science Lab at last week's retreat

Finally, several improvements and polishing throughout netket have been contributed by Jannes Nys , Gabriel Pescia and others. Fermions have now a more intuitive and faster interface, as well as Continuous Space objects, and the documentation of NN architectures improved.

It was very fun to present the "Wasserstein Quantum Monte Carlo" poster, next to Max Welling at #NeurIPS2023. This work was led by my exceptional postdoc Kirill Neklyudov, who unfortunately couldn't attend the conference.

🚨"Can we simulate (ab initio!) the quantum dynamics of strongly correlated electronic systems with variational Monte Carlo?" We answer this question in our latest preprint, arxiv.org/abs/2403.07447 ! (spoiler: yes, we can! 🤯) With Giuseppe Carleo Gabriel Pescia

Our paper on the real-time dynamics of 2d thermal spin systems has now been published in PRB as an Editors' suggestion: journals.aps.org/prb/abstract/1… Computational Quantum Science Lab

New Computational Quantum Science Lab preprint, with Imelda Romero and Jannes Nys ! For those of you interested in computing excited states in periodic systems, check this out arxiv.org/abs/2406.09077, we introduce a neural backflow transformation that allows to accurately target excited states of given

By introducing "symmetry-aware neural backflow transformations", we demonstrate how one can target low-energy excited states of fermionic lattice Hamiltonians, using fermionic neural quantum states. Computational Quantum Science Lab

PRB Editors' Suggestion: Real-time #quantum #dynamics of thermal states with #neural #thermofields J. Nys, Z. Denis, and G. Carleo Phys. Rev. B 109, 235120 ➡️ journals.aps.org/prb/abstract/1… #EdSugg #physics #condmat @APSPhysics Jannes Nys Zakari Denis, Giuseppe Carleo Computational Quantum Science Lab EPFL

Check out our new work on simulating Rydberg devices with >200 atoms! Includes an in-depth analysis of the states encountered during the dynamic state preparation, the topological entanglement entropy, the effects of noise, and more!

Our work on new powerful neural backflow transformations and the application to the homogeneous electron gas is now published in PRB as an Editors' suggestion Computational Quantum Science Lab journals.aps.org/prb/abstract/1…

Deep learning has been successfully used to solve some of the hardest problems in many-body quantum physics, since 2016. Here is a non-exhaustive list: 1. 2D Hubbard model (Fermions pnas.org/doi/full/10.10…, bosons arxiv.org/abs/2404.07869) 2. Out-of-equilibrium Stat Mech