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Reinforcement Learning for Quantum Statistical Physics

Objective

During the last two decades, Machine Learning (ML) and Artificial Intelligence (AI) tools have created a true paradigm shift and impacted numerous fields and industries. In quantum physics, ML is rapidly gaining popularity and is already being extensively used for variational quantum state representation. Recently, a more ambitious and new research direction is developing where Reinforcement Learning agents could be used to solve quantum statistical problems while improving during the task. This field is still in its infancy and is highly promising to yield efficient and scalable computational tools for physics that would be situated between semi-analytical approximations and brute-force Monte Carlo calculations. In this proposal such tools will be developed for applications in modern quantum many-body physics at finite temperatures. In particular, the goal is to train smart AI agents to sample path integrals that occur in various quantum statistical problems. Important research questions include exploring domain generalization where learned knowledge by the agent can be transferred between tasks. The developed methodology will be applied to challenging systems in condensed matter physics such as many-fermion systems and polaronic systems with memory. Besides providing powerful computational tools, this research on the thrilling synthesis of Reinforcement Learning and quantum statistics will yield new insights and perspectives at the forefront of the current AI explosion in physics.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

UNIVERSITEIT ANTWERPEN
Net EU contribution

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.

€ 250 284,72
Address
PRINSSTRAAT 13
2000 Antwerpen
Belgium

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Region
Vlaams Gewest Prov. Antwerpen Arr. Antwerpen
Activity type
Higher or Secondary Education Establishments
Links
Total cost

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.

No data

Partners (1)

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