Quantum computers as learning machines

Can quantum effects help computers to learn? Image (c) VLADGRIN/shutterstock

Can quantum effects help computers to learn? Image (c) VLADGRIN/shutterstock

A new project on Quantum Optimisation and Machine Learning is now underway. Based at the University of Oxford, it's a joint endeavour between the University, Nokia and Lockheed Martin. The aim of the project is to understand the potential for quantum technology to enhance optimisation and machine learning tasks - these are some of the hardest and most important applications in computer science today.

Machine learning refers to a variety of applications where computers figure out 'for themselves' how to perform data analysis, modelling and inference: tasks range from image and speech recognition through to language translation and even genome analysis. Optimisation involves finding the best solution to a problem from a set of alternatives. Generally these areas are regarded as hard for conventional computers, but they are also extremely important: advances in machine learning and optimisation could greatly increase the range of things that computers can do for us. For example, it may allow computers to be smarter at helping people and companies to manage the ever increasing torrent of information flowing from online systems of all kinds (e.g. smartphones). It is believed that harnessing quantum effects can lead to machines that are fundamentally better at machine learning and optimisation, thus unlocking this potential.

Quantum information processing is a field of research and development that hopes to harness the deepest phenomena in physics in order to create whole new kinds of technology. Various approaches are being taken, all of which are of interest to the QuOpaL project. One particularly interesting approach is adiabatic quantum optimisation (and the closely related phenomenon of quantum annealing). Here, a system is initialised to a simple state and then the conditions are slowly ('adiabatically') changed to reach a complex final state that describes the solution to a computational problem of interest. Many believe that this approach is the best way to start using quantum effects for accelerated machine learning -- whether or not this is true is a key topic of interest to the QuOpaL project!

For more information, including positions available, see the QuOpaL project webpage.

Posted on July 26, 2014 .