Quantum Machine Learning on Near-Term Quantum Computers

Lead Research Organisation: University College London
Department Name: London Centre for Nanotechnology

Abstract

Machine Learning has revolutionised the fields of natural language processing, image processing, generative modelling and others. As the field of quantum computing develops, it seems almost natural to ask what benefits the use of quantum computers can have on the field of machine learning - if any. Many existing quantum algorithms for machine learning have been noted for their long-termism and carry with them great assumptions about the capabilities of quantum computers in the future. The more open, and arguably interesting, question is what can the quantum computers of the foreseeable future do for machine learning? Restricting to nearer-term quantum computers requires a more flexible approach. Could hybrid quantum-classical architectures be useful? Can quantum computers aid classical machine learning, instead of replace it? Is there a noise-robust method of learning with a quantum computer? My PhD is concerned with answering these questions by developing new quantum algorithms and testing them by simulation and/or through existing quantum hardware.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/P510270/1 01/04/2016 30/09/2021
2178675 Studentship EP/P510270/1 26/09/2016 01/02/2019 Abdulah Darwish Fawaz