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I have a background in Computer and Electrical Engineering. I obtained my undergraduate degree from the Polytechnic University of Patras, Greece. I received my master's degree in Cloud Computing after graduating from Newcastle University. Following that, I joined the CDT program in 2017 and currently I am in the first year of my PhD in Quantum Machine Learning.
Quantum Machine Learning
Quantum Machine Learning is an emerging interdisciplinary scientific area. It lies in the intersection of Quantum Physics and Machine Learning. The computers that we currently use are built using transistors and the data is stored in the form of binary states 0 and 1. Quantum computers are built using quantum bits (qubits). Qubits can be in multiple states at the same time.
The main advantage of quantum computers is that they could perform complex operations at high speeds. We could then solve certain problems which are currently not feasible.
The aim behind my research is to study the theoretical and applied basis that the effects of quantum techniques can have when applied to classical machine learning.
Quantum Machine Learning can have a great effect on the way we view and understand modern computing. Quantum computing has the potential to make machine learning and AI solutions much faster than classical computing.
The future of AI and machine learning, sped along by quantum computing, looks bright. Real-time human-imitable behaviours is almost a foregone conclusion.
The CDT program is the perfect place for this research to flourish. We have a strong community of researchers, supervisors and industry that can only have a positive impact on my research.