EPSRC Centre for Doctoral Training Cloud Computing for Big Data


Konstantinos Georgopoulos

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.

PhD title

Quantum Machine Learning

Quantum Machine Learning is an emerging interdisciplinary scientific area. It lies in the intersection of Quantum Physics, Statistics and Machine Learning. The computers that we currently use are built using transistors and the data is stored in the form of binary states (bits), 0 and 1. Quantum computers are built using quantum bits (qubits). Qubits can be in multiple states at the same time as a quantum superposition of states.

The main advantage of quantum computers is that they can perform complex operations at high speeds. Thus, they can be used to solve certain problems which are currently computationally infeasible. 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 methods.

Throughout this research I am concerned with two main aspects of quantum machine learning: quantum walks and quantum Markov chain Monte Carlo (MCMC). Quantum walks, due to their intrinsic properties and the vast difference in behaviour compared to classical random walks are a very interesting and promising topic of research. Quantum walks form the basis for many machine learning algorithms, including Markov chain Monte Carlo methods.

Additionally, MCMC methods are vastly used throughout science in many fields, like physics, chemistry, computer science and more. Speeding up these methods through quantisation of MCMC could lead to increase in the computational efficiency. Areas of application include

  • Approximating partition functions in physics
  • Quantum sampling from distributions with at least quadratic increase in efficiency

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.


Paolo Zuliani, Chris Oates, Clive Emary