Quantum Techniques in Machine Learning

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 goal of this workshop is to survey the major results in this new area through a series of invited talks, and to provide an interdisciplinary platform to bring together leading academic scientists, researchers and students in the fields of QC, ML and QML. The workshop will include tutorials that will be geared in particular towards PhD students, as well as a number of contributed presentations of ongoing work on topics such as

  • Quantum computing for enhancing machine learning algorithms
  • Machine learning techniques for the analysis of interacting quantum systems
  • Quantum entanglement and topology for the efficient representation of quantum systems
  • Topological approaches to machine learning based on Topological Quantum Computation
  • Advances of algorithmic techniques for quantum optimisation systems (e.g. quantum annealers).