INQNET Seminar

Monday, January 24, 2022
12:30pm to 1:30pm
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Online Event
Reinforcement learning for designing quantum protocols
Alexey A. Melnikov, Terra Quantum AG, Valiev Institute of Physics and Technology of Russian Academy of Sciences,


Designing quantum protocols and algorithms is difficult and often clashes with our intuition. In our previous research, reinforcement learning was found to be successful in designing quantum optics experiments. Based on the previous success, we apply similar techniques in designing long-distance quantum communication protocols. The same approach allows one to find improved solutions to long-distance communication problems, particularly when dealing with asymmetric situations where the channel noise and segment distance are nonuniform. This is of particular importance in developing long-distance communication schemes, and opens the way to using machine learning in the design and implementation of quantum networks.

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Meeting ID: 933 0458 4361

INQNET (INtelligent Quantum NEtworks & Technologies, inqnet.caltech.edu) is a research program that aims to bring together academia, national laboratories, and industry to advance quantum science and technology and address relevant fundamental questions in physics.

For more information, please contact Nikolai Lauk by email at nlauk@caltech.edu.