SOURCE-LINKED INTELLIGENCE

Nonequilibrium Many Body Control of Quantum Simulators

CORDIS · observation · Publication date unknown

posed 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

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recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR