Quantum Computing (QC) has been for a long time known only for a restricted set of applications where it allows for the achievement of an exponential speed up over the classical computer (e.g. the simulation of quantum physics and chemistry, and the factorisation of large numbers). Recently, however, new developments have opened up opportunities for the application of quantum algorithms to the field of Machine Learning (ML) that may solve problems such as clustering, classification, and pattern matching faster than their classical counterparts. This includes new algorithmic techniques based on Topological Quantum Computation which seem to be especially suitable for kernel-based pattern recognition. The prospects that near term quantum devices could be able to solve computationally hard problems in ML has given rise to Quantum Machine Learning (QML) as a research field in its own right at the intersection between QC and ML. It includes quantum optimisation where theoretical and empirical analysis of quantum annealing approaches are currently subject of intense study.
The QTML 2017 workshop aims to set up a common ground where students and leading researchers in both Quantum Computing and Machine Learning can meet and exchange ideas on the topics of the workshop and discuss how the results from one field can help solving the problems in the other field and viceversa.
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