Filippo Vicentini
@philipVinc
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Postdoc @EPFL, bridging machine learning and quantum science. Coming from Quantum Optics @MPQlab. I was fabricating qubits in a distant past. @Julia supporter
Paris
Joined August 2009
Let me mention that I think this will be a great starting point of a wider effort to package and cleanup NQS dynamics simulation methods. I'd like to collect interested parties that would like to work towards a wide package implementing all recent developments!.
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So if you know how to use #nqs's @NetKetOrg , you can quickly launch simulations yourself for spin systems. I'd like to congratulate Luca, that 'leads by example' by being open, transparent and ensuring total reproducibility of his results!.
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The training scripts to train a 6x6 or 10x10 ViT or ConvNet are included, as well as the usable, well documented, high quality code we used to run those simulations!. This is not a toy library. This is the real one.
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Do you want to simulate large spin systems with NQS? Yourself? Automatically? . @GravinaLuca97 has just released the accompanying code for the Systematic study of projected neural dynamics, done in collaboration with @vinniesavon . Check it out!
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FYI: Ecole Polytechnique offers several benefits compared to other French academic positions (CNRS/Maitre de conference), including the possibility to negotiate startup packages, higher pay and reduced teaching load.
Full time Professorship openings: Ecole Polytechnique will recruit 2 full time assistant professors in Condensed Matter Physics and in Quantum Physics. If you're working at the intersection of AI/ML and Quantum Physics or Material Science that's a plus!. For info, get in touch!.
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Full time Professorship openings: Ecole Polytechnique will recruit 2 full time assistant professors in Condensed Matter Physics and in Quantum Physics. If you're working at the intersection of AI/ML and Quantum Physics or Material Science that's a plus!. For info, get in touch!.
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RT @MarinBukov: hoping to see many of you at this upcoming machine learning 4 quantum matter workshop @mpi_pks in Dresden (Feb 24-28, '25):….
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RT @gppcarleo: Our endeavor on variational benchmarks for quantum many-body problems is now published in @ScienceMagazine! . In this large….
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About code: it's spaghetti right now, and relies on an hacked version of @NetKetOrg extensively. We had to get the paper out (grants. ) but we'll be cleaning it up and share it soon!. This project also triggered us to start thinking about NetKet 4. Stay tuned!.
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Overall, the adaptive hyperparameter tuning is THE thing that has made this possible. You know all those optimisations that explode, those that are suboptimal? Well, this never happens. The simulations are very stable. I'm optimistic this will be THE way forward.
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The major takeaways are "the rules for efficient integration schemes (not RK!)", "Which estimator for the infidelity gradient", "adaptive optimization hyperparameter tuning (learning rate, SR regularisation)" and Dynamics with ViT. The latter might be applicable to VMC as well.
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Neural Quantum States + Dynamics: Together with @GravinaLuca97 and @vinniesavon from @EPFL_en, we have studied all aspects of NQS projective methods to integrate Schroedinger eq. Long paper but easy read. Lots of recap tables!.
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My precise use case: I write 'training loop-like' code, users come with their NNX/Linen/Equinox modules. I wish to supports out of the box static information, frozen (nondiff) parameters, batch norm, and possibly more crazy stuff I did not think of for all those frameworks!.
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Wishlist: As a 'higher-level' sciML library author (@NetKetOrg) , with end-users that want to use both equinox and linen/nnx I would love if @PatrickKidger and @cgarciae88 agreed on a common underlying API to filter and manipulate both their frameworks.
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RT @FerlainoGroup: 🚨 Big news on arXiv! 🚨 Over 30 female Quantum Professors have united to release a powerful Manifesto of Values. They ref….
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Sigh 😪.
Italy's real wage misery: inflation-adjusted wages in 2023 were 4.4% lower than in 1990. Even real wages in Greek performed a little better.
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In my eyes Academia is about open science, confrontation and advancing the little domains we chose to contribute. If we actively prevent this, we're no different from Google and other industrial players.
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Code is not just reproducible, it's a source of truth! Many details are lost when code is paraphrased into a manuscript. .
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Computational sciences are perfectly 'reproducible' by default, we just need to share codes. This has great benefits: it's easier to 'enter' a new field, it's easier to compare results, it's easer to advance. Yet many physicists don't share it for fear of losing competitiveness.
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