🌟🚀 2023's Top Papers on Neural Quantum States! A year of many innovative approaches blending ML with quantum science. Here are my top picks: 1. Fast Quantum Natural Gradient (SR), our favorite optimizer, now scaling linearly for small batch sizes bit.ly/41FDnp1 2. Fidelity with variance-reduced estimators, and projected tVMC without bias bit.ly/41upA4J 3. Excited states with an extended-space determinant trick bit.ly/3NCOjhi 4. Neural Pfaffians in continuous space for electron pairing and more bit.ly/3GV2xqa 5. Generalized Neural Wave Functions for chemistry bit.ly/3NCOvx2 6. Free-Energy optimization for dense hydrogen bit.ly/3Ryz2zt 📈 Overall, we have seen incredible progress on hard benchmarks, and NQS are the most accurate techniques now available for many ground-state problems: e.g., the electron gas bit.ly/3NCkugN, the J1-J2 model bit.ly/3NCd0dW, and much more bit.ly/3NDMtgp. Most of the methodological developments above are available or will be available shortly in netket.org/, so make sure to check it out and discover the future of NQS with @NetKetOrg ! #NeuralQuantumStates #ML4Science #TopPicks2023

We had a very productive @cqs_lab group retreat last week, enjoying the beauty of southern Italy (Greek temples, pizzas, and buffaloes included) and discussing quantum physics, @NetKetOrg and more!

It's time for another brand new @NetKetOrg tutorial! This week, thanks to @pgabbo0 of @cqs_lab @EPFL, we explore bosonic systems in continuous space: first, a 3D harmonic oscillator and then 1D confided particles with Gaussian interactions, using a DeepSet NQS netket.readthedocs.io/en/lat…

Started in 2018, NetKet @NetKetOrg was the first open-source library for machine learning and many-body quantum physics ever released, and we are proud to see it being used by an increasing number of researchers! With version 3 (released in 2021 thanks to the efforts of many and coordinated by @philipVinc @cqs_lab @EPFL), NetKet has acquired many new functionalities, some of which are less known, including support for systems in continuous space, fermions, dynamics, and much more. In fact, it is the only library with full support for essentially all of the applications of neural quantum states across domains, from lattice spin models to the electron gas. To help discover all these fantastic functionalities, we are progressively releasing new Tutorials. We start today with a new Tutorial on Lattice fermions, from Slater determinants to Neural Backflow. Have a look here to learn how to treat strongly interacting lattice fermions with fermionic NQS: netket.readthedocs.io/en/lat… Finally, thank you @JannesNys for implementing the much-needed support for lattice fermions used (also) in this Tutorial!