Descripción del proyecto
La simulación cuántica y la geometría de la hoja de caucho arrojan nueva luz sobre distintos sistemas cuánticos
La topología es una rama de las matemáticas que se ocupa del estudio de las formas y su disposición en el espacio. A veces denominada geometría de la hoja de caucho, se interesa por las propiedades de los objetos que se conservan bajo deformaciones continuas como al estirarlos y doblarlos. Históricamente se circunscribió al ámbito de las matemáticas. Con todo, su aplicación a la física de los materiales aporta una perspectiva única, reconocida en parte por el Premio Nobel de Física de 2016 por el papel de la topología en la comprensión de las formas exóticas de la materia. El proyecto financiado con fondos europeos NOQIA ampliará considerablemente este cruce de caminos mediante el desarrollo de nuevos métodos teóricos y descripciones. El proyecto unirá la simulación cuántica y los efectos y sistemas topológicos con el ámbito de los fenómenos ultrarrápidos y la atociencia, el aprendizaje automático cuántico y las redes neurales cuánticas.
Objetivo
Quantum simulators (QS) are experimental systems that allow mimic hard to simulate models of condensed matter, high energy physics and beyond. QS have various platforms: from ultracold atoms and ions to superconducting qubits. They constitute the important pillar of quantum technologies (QT), and promise future applications in chemistry, material science and optimization problems. Over the last decade, QS were particularly successful in mimicking topological effects in physics (TEP) and in developing accurate quantum validation/certification (QVC) methods. NOQIA is a theory project, aimed at introducing the established field of QS+TEP+QVC into two novel areas: physics of ultrafast phenomena and attoscience (AS) on one side, and quantum machine learning (ML) and neural networks (NN) on the other. This will open up new horizons/opportunities for research both in AS and in ML/NN. For instance, in AS we will address the question if intense laser physics may serve as a tool to detect topological effects in solid state and strongly correlated systems. We will study response of matter to laser pulses carrying topological signatures, to determine if they can induce topological effects in targets. We will design/analyze QS using trapped atoms to understand and detect TEP in the AS. On the ML/NN side, we will apply classical ML to analyze, design and control QS for topological systems, in order to understand and optimize them. Conversely, we will transfer many-body techniques to ML in order to analyze and possibly improve performance of classical machine learning. We will design and analyze quantum neural network devices that will employ topology in order to achieve robust quantum memory or information processing. We will design/study attractor neural networks with topological stationary states, or feed-forward networks with topological Floquet and time-crystal states. Both in AS and ML/NN, NOQIA will rely on quantum validation and certification protocols and techniques.
Ámbito científico
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencesdata sciencedata processing
- natural sciencesphysical sciencesopticslaser physics
- natural sciencesphysical sciencestheoretical physics
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Palabras clave
Programa(s)
Régimen de financiación
ERC-ADG - Advanced GrantInstitución de acogida
08860 Castelldefels
España