Funded PhD position in quantum machine learning
We invite applications for a PhD position on “Quantum machine learning” which is part of the newly funded Cluster of Excellence MATH+ within the Berlin research landscape.
Recently the highly interdisciplinary field of quantum machine learning has emerged and enjoyed significant interest. In this new field, both the underlying foundations of statistical learning theory and the associated applied machine learning techniques have begun to be extended to the setting of both quantum algorithms and data-sets emerging from a quantum context. In particular, exciting proof-of-principle algorithms and results have been obtained in all three newly emerging branches of this rapidly developing field. In particular, the application of classical learning algorithms to data-sets of a quantum origin (classical-quantum) has yielded new techniques for decoding of topological quantum error correcting codes, the identification of phases and the design of novel experiments.
Simultaneously, new quantum algorithms, for essential learning-sub routines such as matrix inversion and gradient descent, have been developed for application to classical data (quantum-classical), and finally, in the quantum-quantum context, in which quantum algorithms are applied to quantum data-sets while there are still many open questions, recent years have seen advances such as the development of a novel reinforcement learning framework for quantum agents in quantum environments. The goal of this project is to establish a mathematical methodology for instances of quantum machine learning, understanding its statistical basis, and at the same time to explore practical applications.
For inquiries, please send an email to email@example.com with "Quantum machine learning" in the subject line.
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Link to the research group led by Jens Eisert:
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