Carregant...
Tipus de document
Treball de fi de grauData de publicació
Llicència de publicació
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/222370
Tensor Networks for Quantum-Inspired Simulations
Títol de la revista
Autors
Director/Tutor
ISSN de la revista
Títol del volum
Recurs relacionat
Resum
Quantum algorithms have the potential to accelerate computation and reduce memory requirements on advanced quantum computers. However, current hardware limitations hinder their application to complex problems. In this work, we investigate a promising approach that bypasses the need for quantum hardware by leveraging tensor networks to simulate quantum algorithms on classical computers. We assess the performance of quantum-inspired simulators relative to classical methods in terms of memory, runtime, and accuracy. Our results demonstrate that quantum-inspired simulators can surpass their classical counterparts in accuracy while using less than half the memory. Additionally, we show that operators based on higher-precision approximations can reduce errors in quantum-inspired simulations without compromising memory requirements. Finally, we explore the capability of quantum-inspired simulators to address memory-intensive problems beyond the reach of conventional algorithms.
Descripció
Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2025, Tutors: Bruno Julià-Díaz, Artur García-Saez, Stefano Carignano
Matèries (anglès)
Citació
Col·leccions
Citació
BENARROCH JEDLICKI, Jack. Tensor Networks for Quantum-Inspired Simulations. [consulta: 23 de gener de 2026]. [Disponible a: https://hdl.handle.net/2445/222370]