Hamming distance kernelisation via topological quantum computation

Di Pierro, Alessandra, Mengoni, Riccardo, Nagarajan, Rajagopal ORCID: https://orcid.org/0000-0002-9724-4962 and Windridge, David ORCID: https://orcid.org/0000-0001-5507-8516 (2017) Hamming distance kernelisation via topological quantum computation. Theory and Practice of Natural Computing TPNC 2017. Lecture Notes in Computer Science, vol 10687. In: 6th International Conference on the Theory and Practice of Natural Computing (TPNC 2017, 18-20 Dec 2017, Prague, Czech Republic. ISBN 9783319710686. ISSN 0302-9743 [Conference or Workshop Item] (doi:10.1007/978-3-319-71069-3_21)

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Abstract

We present a novel approach to computing Hamming distance and its kernelisation within Topological Quantum Computation. This approach is based on an encoding of two binary strings into a topological Hilbert space, whose inner product yields a natural Hamming distance kernel on the two strings. Kernelisation forges a link with the field of Machine Learning, particularly in relation to binary classifiers such as the Support Vector Machine (SVM). This makes our approach of potential interest to the quantum machine learning community.

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