ELFT HÍRADÓ

[Dr. Dóra Balázs <dora.balazs AT ttk.bme.hu>]

                      MEGHÍVÓ








    a Szilárd Leó-Kollokvium előadására:

               Richard Kueng
      (Johannes Kepler University)





“Learning to predict ground state properties of gapped Hamiltonians"

Kivonat: Classical machine learning (ML) provides a potentially powerful 
approach to solving challenging quantum many-body problems in physics and 
chemistry. However, the advantages of ML over traditional methods have not 
been firmly established. In this work, we prove that classical ML algorithms 
can efficiently predict ground-state properties of gapped Hamiltonians after 
learning from other Hamiltonians in the same quantum phase of matter. By 
contrast, under a widely accepted conjecture, classical algorithms that do 
not learn from data cannot achieve the same guarantee. Our proof technique 
combines signal processing with quantum many-body physics and also builds 
upon the recently developed framework of classical shadows. I will try to 
convey the relevant background and main ideas and also present numerical 
experiments that address the anti-ferromagnetic Heisenberg model and Rydberg 
atom systems.


Helye: BME Fizikai Intézet, Fizika Tanszék
Budafoki út 8. F-épület, 2. emelet, 13-as terem


Ideje: 2025. november 25. (kedd), 14:30.
Kapunyitás 14:14-től.
Program: http://physics.bme.hu/kollokvium


Minden érdeklődőt örömmel várunk!





Dóra Balázs és Zaránd Gergely