Filippo Vicentini
@philipvinc.bsky.social
Professor of Quantum Physics and AI at Ecole Polytechnique in Paris.
Core contributor to the Neural Quantum State simulation methods, and to open science efforts.
Core contributor to the Neural Quantum State simulation methods, and to open science efforts.
For 'simple' problems sampling α=2 seems good, but for harder ones you benefit from an over dispersed distribution.
Worry not! This is FREE and you can even autotune α so VMC is not harder.
You can find a #NetKet based repository, fully reproducible github.com/NeuralQXLab/...
Worry not! This is FREE and you can even autotune α so VMC is not harder.
You can find a #NetKet based repository, fully reproducible github.com/NeuralQXLab/...
July 9, 2025 at 12:49 PM
For 'simple' problems sampling α=2 seems good, but for harder ones you benefit from an over dispersed distribution.
Worry not! This is FREE and you can even autotune α so VMC is not harder.
You can find a #NetKet based repository, fully reproducible github.com/NeuralQXLab/...
Worry not! This is FREE and you can even autotune α so VMC is not harder.
You can find a #NetKet based repository, fully reproducible github.com/NeuralQXLab/...
We wondered "Are we sampling right?" And the answer seems "probably no". When doing #VMC on hard #quantum problems, |ψ(x)|² is optimal to get good "Energy estimates" but you need precise gradients to converge!
Turns out, you converge better by looking at |ψ(x)|ᵅ .
arxiv.org/abs/2507.05352
Turns out, you converge better by looking at |ψ(x)|ᵅ .
arxiv.org/abs/2507.05352
July 9, 2025 at 12:49 PM
We wondered "Are we sampling right?" And the answer seems "probably no". When doing #VMC on hard #quantum problems, |ψ(x)|² is optimal to get good "Energy estimates" but you need precise gradients to converge!
Turns out, you converge better by looking at |ψ(x)|ᵅ .
arxiv.org/abs/2507.05352
Turns out, you converge better by looking at |ψ(x)|ᵅ .
arxiv.org/abs/2507.05352
We also add all the states we obtained through our simulations, and you can compute quantities on them yourself. Just `nqxpack.load("data/states/t=XYZ.nk` and compare to our data!
The training scripts to train a 6x6 or 10x10 ViT or ConvNet are included, as well as an usable, well documented code.
The training scripts to train a 6x6 or 10x10 ViT or ConvNet are included, as well as an usable, well documented code.
February 25, 2025 at 2:22 PM
We also add all the states we obtained through our simulations, and you can compute quantities on them yourself. Just `nqxpack.load("data/states/t=XYZ.nk` and compare to our data!
The training scripts to train a 6x6 or 10x10 ViT or ConvNet are included, as well as an usable, well documented code.
The training scripts to train a 6x6 or 10x10 ViT or ConvNet are included, as well as an usable, well documented code.
Do you want to simulate large spin systems with NQS? Yourself? Automatically?
We have just released the accompanying code for the Systematic study of projected neural dynamics, together with Luca Gravina and Vincenzo Savona!
Check it out! github.com/NeuralQXLab/...
We have just released the accompanying code for the Systematic study of projected neural dynamics, together with Luca Gravina and Vincenzo Savona!
Check it out! github.com/NeuralQXLab/...
February 25, 2025 at 2:22 PM
Do you want to simulate large spin systems with NQS? Yourself? Automatically?
We have just released the accompanying code for the Systematic study of projected neural dynamics, together with Luca Gravina and Vincenzo Savona!
Check it out! github.com/NeuralQXLab/...
We have just released the accompanying code for the Systematic study of projected neural dynamics, together with Luca Gravina and Vincenzo Savona!
Check it out! github.com/NeuralQXLab/...