Alessandro Sinibaldi

I am giving an invited talk tomorrow (March 17 at 3:30 PM in Room 507) at #APSGlobalPhysics26 in Denver, if you are around and want to catch up let me know !

It's the second time in less than a week that an @arxiv submission from my group is placed on hold (quant-ph). Never happened to me before, what's going on?

Amazing news! Riccardo Rossi of @cqs_lab @EPFL_en has been recognized with the Hermann Kümmel Early Achievement Award for "groundbreaking advances in computational quantum field theory for many-fermion problems, including determinant algorithms for connected-diagram expansions and resummation techniques, leading to key results on the unitary Fermi gas and the Hubbard model". Congratulations Riccardo, so well deserved! actu.epfl.ch/news/riccardo-r…

If you want to stay current on a challenging problem in quantum physics, bookmark this: hasthehubbardmodelbeensolved…

Today, I got tenure. It has been a tough, long journey, started 17 years ago with the beginning of my PHD @Sissaschool. 4 Countries, 5 Different Cities, and ~80 papers later, I found my academic home @EPFL . I want to thank all present and past members of @cqs_lab @EPFL for their dedication and hard work; all the kind people who have supported me during these years, especially @MompeanBotias; Sandro Sorella and all the fantastic people from whom I have learnt the things I know; all the people who have hated my work; all the people who have loved my work. Finally, let me also thank @vinniesavon, for being a great mentor in the past 3 years in Lausanne. ethrat.ch/en/appointments-ma…

🌟🚀 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

The new work by @IBMResearch Quantum is an important milestone in the field. nature.com/articles/s41586-0… I especially like the shift from generating "supremely" useless random numbers to the task of simulating something of "utility" for physics. But..... (1/6)

Check this thread out! 👇 We show that the commonly adopted Monte Carlo estimator for the quantum geometric tensor (a.k.a. Fisher) can be biased! We introduce an alternative approach to t-Vmc, opening up to the possibility of accurately simulating measurement-induced phase transitions in 2d systems. Paper at arxiv.org/abs/2305.14294