Study of nanostructures coupled with a superconducting lead via the neural-network quantum states methods.n

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081723%3A_____%2F25%3A00642973" target="_blank" >RIV/68081723:_____/25:00642973 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.37904/metal.2025.5156" target="_blank" >http://dx.doi.org/10.37904/metal.2025.5156</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.37904/metal.2025.5156" target="_blank" >10.37904/metal.2025.5156</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Study of nanostructures coupled with a superconducting lead via the neural-network quantum states methods.n

Popis výsledku v původním jazyce

The Neural-Network Quantum States (NNQS) method is rapidly emerging as a powerful tool for investigating nquantum many-body physics. By combining variational Monte Carlo techniques with neural network-based nvariational functions, this approach leverages the remarkable advancements in deep learning achieved in nrecent years. While substantial progress has been made in simulating magnetic systems on lattices, simple nmolecules, and even continuous systems, the ab initio simulation of complex strongly correlated electron nsystems continues to pose significant challenges. With the help of the generalized atomic limit (GAL) – a nrecently developed model describing a system of quantum dots coupled to a superconducting lead – we nattempt to show the efficiency and accuracy of NNQS, mainly the Restricted Boltzmann Machine, by comparing nthe acquired results to other available methods such as exact diagonalization. The simultaneous study of the nsystem’s properties such as the energy spectrum and quantum phase transitions could bring advancements nin electronics, sensors or the design of high-quality qubits used in quantum computers.

Název v anglickém jazyce

Study of nanostructures coupled with a superconducting lead via the neural-network quantum states methods.n

Popis výsledku anglicky

The Neural-Network Quantum States (NNQS) method is rapidly emerging as a powerful tool for investigating nquantum many-body physics. By combining variational Monte Carlo techniques with neural network-based nvariational functions, this approach leverages the remarkable advancements in deep learning achieved in nrecent years. While substantial progress has been made in simulating magnetic systems on lattices, simple nmolecules, and even continuous systems, the ab initio simulation of complex strongly correlated electron nsystems continues to pose significant challenges. With the help of the generalized atomic limit (GAL) – a nrecently developed model describing a system of quantum dots coupled to a superconducting lead – we nattempt to show the efficiency and accuracy of NNQS, mainly the Restricted Boltzmann Machine, by comparing nthe acquired results to other available methods such as exact diagonalization. The simultaneous study of the nsystem’s properties such as the energy spectrum and quantum phase transitions could bring advancements nin electronics, sensors or the design of high-quality qubits used in quantum computers.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10302 - Condensed matter physics (including formerly solid state physics, supercond.)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

METAL 2025. Proceedings 34th International Conference on Metallurgy and Materials

ISBN

978-80-88365-27-3

ISSN

2694-9296

e-ISSN

—

Počet stran výsledku

6

Strana od-do

464-469

Název nakladatele

TANGER

Místo vydání

Ostrava

Místo konání akce

Brno

Datum konání akce

21. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—