Project description
Advancing nonequilibrium quantum control with AI-driven quantum simulators
Controlling quantum matter in out-of-equilibrium states presents a significant challenge in modern physics. The ERC-funded QuSimCtrl project aims to address this by expanding control from equilibrium to out-of-equilibrium states. Using quantum simulation, it will explore phenomena not seen in conventional materials and tackle the challenge of manipulating intensely driven nonequilibrium systems. Integrating quantum control with AI, particularly reinforcement learning (RL), the project will develop frameworks for nonadiabatic many-body state control, improving manipulation in cold atoms, trapped ions, and quantum solids. Its goal is to uncover guiding principles for many-body control away from equilibrium, linking quantum dynamics, statistical mechanics, and machine learning. This research is crucial for innovating materials and technologies based on nonequilibrium processes in condensed matter, quantum optics and quantum computing.
Objective
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.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2023-STG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
80539 MUNCHEN
Germany
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.