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 nd 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.
Topic: INQNET seminar
Time: January 24 , 2022 12:30 PM Pacific Time (US and Canada)
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https://caltech.zoom.us/j/93304584361
Meeting ID: 933 0458 4361
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Nikolai Lauk