Hamming distance kernelisation via topological quantum computation

Di Pierro, Alessandra, Mengoni, Riccardo, Nagarajan, Rajagopal and Windridge, David ORCID: https://orcid.org/0000-0001-5507-8516 (2017) Hamming distance kernelisation via topological quantum computation. In: 6th International Conference on the Theory and Practice of Natural Computing (TPNC 2017, 18-20 Dec 2017, Prague, Czech Republic. (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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Cite this paper as: Di Pierro A., Mengoni R., Nagarajan R., Windridge D. (2017) Hamming Distance Kernelisation via Topological Quantum Computation. In: Martín-Vide C., Neruda R., Vega-Rodríguez M. (eds) Theory and Practice of Natural Computing. TPNC 2017. Lecture Notes in Computer Science, vol 10687. Springer, Cham
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 23289
Notes on copyright: This is a post-peer-review, pre-copyedit version of an article published in Theory and Practice of Natural Computing TPNC 2017. Lecture Notes in Computer Science, vol 10687. The final authenticated version is available online at Springer via http://dx.doi.org/10.1007/978-3-319-71069-3_21
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Depositing User: David Windridge
Date Deposited: 09 Jan 2018 14:21
Last Modified: 04 Apr 2019 05:17
ISBN: 9783319710686
URI: https://eprints.mdx.ac.uk/id/eprint/23289

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