Current students


Section: Computer Science and Engineering

Major Research topic:
Applied Quantum Machine Learning

Quantum computing is a promising computational architecture that uses quantum systems as means of
computation and promises to revolutionize many fields. Several algorithms have been developed to
pave the way for innovations in chemistry, machine learning, operations research, cybersecurity,
finance, healthcare etc.
The implementation of such algorithms on gate-based quantum computers are constrained to current
hardware limitations, hindering at present time their full potential. A hopeful alternative is offered by
quantum annealers, special purpose machines that are able to exploit quantum fluctuations in order to
provide a near-optimal solution (i.e. find low-energy states) to combinatorial optimization problems.
The readily availability of quantum annealers via cloud services - such as D-Wave machines - has
fueled research in experiments and proof of concepts towards gaining quantum advantage. However, it
is still unclear at the present time whether the employment of quantum annealers in solving industrial
and commercial problems is a viable alternative; the actual implementation of quantum algorithms on
the device and their execution, are subject to several practical and heuristical factors that have not
been fully addressed yet.
The direction of my project aims towards exploring classical NP-hard problems that might gain an
advantage if run on quantum devices.