Machine Learning for Quantum Networking

We live in an age governed by information, and with the emergence of quantum technologies, the security of classical methods of communication and information sharing are threatened. This emergence has introduced the concepts of quantum networking and quantum communication protocols that can not just significantly enhance computational power, but also increase the security for secret sharing protocols.

In my PhD, I will be developing machine learning techniques for quantum networking, which will exploit the power of quantum theory, and take us one step closer to the reality of a quantum internet. These quantum communication protocols rely on a shared entangled resource, which is extremely sensitive to environmental effects.

Therefore, to successfully implement these communication protocols in a noisy environment, one must consider a different quantum correlation known as quantum discord, which is less powerful than entanglement, but more robust to noise. I aim to develop algorithms that implement well-known quantum communication protocols like quantum secret sharing and quantum telecloning with a discordant resource, and then compare their performance with respect to their entanglement-based counterparts.