Descripción del proyecto
Nuevos métodos para el desarrollo de algoritmos cuánticos
El aprendizaje automático cuántico es un área muy interesante en el campo de nueva aparición de la computación cuántica. Sin embargo, puesto que los últimos avances se basan principalmente en métodos heurísticos que aún no se han podido probar adecuadamente, seguimos sin tener una comprensión sólida de la teoría del aprendizaje automático cuántico. Teniendo esto en cuenta, el objetivo del proyecto QuantOrder, financiado con fondos europeos, es definir nuevos métodos para el desarrollo de algoritmos cuánticos y mejorar aspectos de la teoría del aprendizaje automático cuántico. Todas las ideas de esta iniciativa se centrarán en la búsqueda de estructuras a gran escala en diferentes objetos, gracias a la eficacia de los ordenadores cuánticos en el reconocimiento de patrones.
Objetivo
Quantum computing is an emerging, interdisciplinary field of science in the intersection of computer science, mathematics and physics. Recent experimental advances in building a physical quantum computer show the urgency of finding possible applications. On the other hand to date we only have very small quantum computers, which are mostly useful for proof of concept demonstrations, thus for the time being one needs to focus on building and understanding the underlying mathematical theory. A particularly interesting aspect of quantum computing is quantum machine learning, which also needs a more firm theoretical understanding, because many of the recent developments are based on heuristic approaches which cannot be properly tested yet, due to the limitations of the available hardware.
This proposal outlines new approaches and ideas for quantum algorithm development, and attempts to improve some aspects of the theory of quantum machine learning, while also encompasses some fundamental theoretical questions. The described ideas are all related to the problem of finding large-scale structures in various objects. Since quantum computers tend to be quite efficient at recognizing patterns, it is a promising angle of approach. The relevant ideas are inspired by multiple related disciplines, and several of the proposed tools were recently co-developed by the applicant.
The supervisor has an outstanding track record in developing the mathematical theory of large-scale structures emerging in graphs, groups and networks, while the applicant has demonstrated strong problem solving skills and the ability of developing novel quantum algorithms, which promises a fruitful collaboration in the implementation of the proposed action.
Ámbito científico
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwarequantum computers
- natural sciencesmathematics
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencesartificial intelligenceheuristic programming
Palabras clave
Programa(s)
Régimen de financiación
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
1053 Budapest
Hungría