QuSimCtrl
Financiado
Nonequilibrium Many Body Control of Quantum Simulators
The ability to control quantum matter in a state of equilibrium is a milestone of 20th-century physics. A major goal of modern physics is to extend this knowledge to out-of-equilibrium systems. Located at the boundary between equi...
The ability to control quantum matter in a state of equilibrium is a milestone of 20th-century physics. A major goal of modern physics is to extend this knowledge to out-of-equilibrium systems. Located at the boundary between equilibrium and nonequilibrum, quantum simulation appears particularly suitable for this purpose. Using periodic drives, quantum simulators can experimentally emulate phenomena hitherto inaccessible in conventional materials, such as artificial gauge fields or topological and dynamically localized matter. However, our understanding of how to manipulate systems exposed to intense nonequilibrium drives is in its infancy, especially regarding strongly interacting models.
We propose to overcome the current limitations by combining ideas from quantum control and artificial intelligence (AI) algorithms. We will develop a new theoretical framework for nonadiabatic many-body state control on top of strong periodic drives underlying the optimal manipulation of ordered prethermal states of matter without equilibrium counterparts. Understanding this many-body dynamics will improve cutting-edge manipulation techniques in cold atoms, trapped ions, superconducting circuits, and quantum solids.
We will add reinforcement learning (RL), one of the most promising techniques in AI, to the quantum entanglement control toolbox. Deep RL has the potential to push the state-of-the-art of (dis-)entangling quantum states since it is capable of identifying effective degrees of freedom even when no underlying physical structure is immediately obvious.
Discovering guiding principles of physics for many-body control away from equilibrium has the potential to reveal new connections across quantum dynamics, statistical mechanics, optimal control, and machine learning. The proposed research establishes a missing link on the roadmap for designing future materials and technologies based on nonequilibrium processes in condensed matter, quantum optics, and quantum computing.
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Duración del proyecto: 60 meses
Fecha Inicio: 2023-11-17
Fecha Fin: 2028-11-30
Fecha Fin: 2028-11-30
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-11-17
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2023-STG:
ERC STARTING GRANTS
Cerrada
hace 2 años
Presupuesto
El presupuesto total del proyecto asciende a
2M€
Líder del proyecto
MAXPLANCKGESELLSCHAFT ZUR FORDERUNG DER WISSE...
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Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5