Description du projet
La simulation quantique et la géométrie de la feuille de caoutchouc font la lumière sur les systèmes quantiques divers
La topologie est une branche des mathématiques qui étudie les formes et leurs dispositions dans l’espace. Parfois appelée géométrie de la feuille de caoutchouc, cette branche s’intéresse aux propriétés des objets préservés en dépit de déformations continues telles que l’étirement et le pliage. Ce domaine a été historiquement confiné au champ des mathématiques. Toutefois, son application à la physique des matériaux fournit des informations uniques, en partie reconnues par le prix Nobel de physique 2016, pour le rôle de la topologie dans la compréhension de formes exotiques de la matière. Le projet NOQIA, financé par l’UE, élargira ce carrefour interdisciplinaire de manière considérable en développant des approches et des descriptions théoriques innovantes. Le projet combinera la simulation quantique et les effets et systèmes topologiques au champ des phénomènes ultrarapides et à l’attophysique, à l’apprentissage automatique quantique et aux réseaux neuronaux quantiques.
Objectif
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.
Champ scientifique
- 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
Mots‑clés
Programme(s)
Thème(s)
Régime de financement
ERC-ADG - Advanced GrantInstitution d’accueil
08860 Castelldefels
Espagne