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Neural-Network Quantum States for Entanglement Phase Transitions

Jun 13, 2023, 11:00 AM
Institut Pascal

Institut Pascal

Rue André Rivière 91400 Orsay


Filippo Vicentini (Ecole Polytechnique)


The success of Machine Learning owes to the development of neural-networks, variational approximators that can efficiently represent unknown functions living in high-dimensional spaces. Recently, those techniques have been ported to the field of numerical physics and used to approximate inherently high dimensional objects such as the Many-Body Wave-Function [1] or Density-Matrix [2] in an approach generally known as Neural-Network Quantum States (See Ref.3 for a general introduction).

In this seminar I will first discuss why estabilished approaches in NQS literature are incompatible with the simulation of the dynamics undergoing strong measurements, and then I will present a new approach that allowed us to avoid previous limitations [4].
Finally, I will showcase some recent results on the entanglement dynamics of large 2-Dimensional, non integrable systems.

[1] Carleo and Troyer, Science 355, 602 (2017)
[2] F Vicentini, A Biella, N Regnault, C Ciuti, Phys. Rev. Lett 122 (25), 250503
[3] A. Dawid et Al, arXiv:2204.04198 (2022)
[4] A. Sinibaldi, C. Giuliani, G. Carleo, F. Vicentini, arXiV: 2305.14294 (2023)

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