Hamming distance kernelisation via topological quantum computation

Conference paper

Di Pierro, A., Mengoni, R., Nagarajan, R. and Windridge, D. 2017. Hamming distance kernelisation via topological quantum computation. Martín-Vide C., Neruda R. and Vega-Rodríguez M. (ed.) 6th International Conference on the Theory and Practice of Natural Computing. Prague, Czech Republic 18 - 20 Dec 2017 Springer. pp. 269-280 https://doi.org/10.1007/978-3-319-71069-3_21
TypeConference paper
TitleHamming distance kernelisation via topological quantum computation
AuthorsDi Pierro, A., Mengoni, R., Nagarajan, R. and Windridge, D.
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.

KeywordsQuantum computing; Topology; Kernel function
Conference6th International Conference on the Theory and Practice of Natural Computing
Page range269-280
Proceedings TitleTheory and Practice of Natural Computing: 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, Proceedings
SeriesLecture Notes in Computer Science
EditorsMartín-Vide C., Neruda R. and Vega-Rodríguez M.
ISSN0302-9743
Electronic1611-3349
ISBN
Paperback9783319710686
Electronic9783319710693
PublisherSpringer
Publication dates
Online19 Nov 2017
Print19 Nov 2017
Publication process dates
Deposited09 Jan 2018
Accepted11 Sep 2017
Output statusPublished
Accepted author manuscript
File Access Level
Open
Copyright Statement

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

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

Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-319-71069-3_21
Scopus EID2-s2.0-85041281892
Web of Science identifierWOS:000450354700021
Web address (URL) of conference proceedingshttps://doi.org/10.1007/978-3-319-71069-3
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LanguageEnglish
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Tatarchuk, A., Sulimova, V., Torshin, I., Mottl, V. and Windridge, D. 2014. Supervised selective kernel fusion for membrane protein prediction. Comin, M., Käll, L., Marchiori, E., Ngom, A. and Rajapakse, J. (ed.) 9th IAPR International Conference Pattern Recognition in Bioinformatics (PRIB 2014). Stockholm, Sweden 21 - 23 Aug 2014 Springer. pp. 98-109 https://doi.org/10.1007/978-3-319-09192-1_9
Challenges in designing an online healthcare platform for personalised patient analytics
Poh, N., Tirunagari, S. and Windridge, D. 2014. Challenges in designing an online healthcare platform for personalised patient analytics. 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD). Orlando, FL., USA 09 - 12 Dec 2014 IEEE. pp. 1-6 https://doi.org/10.1109/CIBD.2014.7011526
Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses
Tirunagari, S., Poh, N., Aliabadi, K., Windridge, D. and Cooke, D. 2014. Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses. 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Orlando, FL., USA 09 - 12 Dec 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 304-309 https://doi.org/10.1109/CIDM.2014.7008682
Non-enumerative cross validation for the determination of structural parameters in feature-selective SVMs
Chernousova, E., Levdik, P., Tatarchuk, A., Mottl, V. and Windridge, D. 2014. Non-enumerative cross validation for the determination of structural parameters in feature-selective SVMs. 22nd International Conference on Pattern Recognition ICPR 2014. Stockholm, Sweden 24 - 28 Aug 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 3654-3659 https://doi.org/10.1109/ICPR.2014.628
Linear regression via elastic net: non-enumerative leave-one-out verification of feature selection
Chernousova, E., Razin, N., Krasotkina, O., Mottl, V. and Windridge, D. 2014. Linear regression via elastic net: non-enumerative leave-one-out verification of feature selection. in: Aleskerov, F., Goldengorin, B. and Pardalos, P. (ed.) Clusters, Orders, and Trees: Methods and Applications: In Honor of Boris Mirkin's 70th Birthday New York Springer.
Automatic annotation of tennis games: an integration of audio, vision, and learning
Yan, F., Kittler, J., Windridge, D., Christmas, W., Mikolajczyk, K., Cox, S. and Huang, Q. 2014. Automatic annotation of tennis games: an integration of audio, vision, and learning. Image and Vision Computing. 32 (11), pp. 896-903. https://doi.org/10.1016/j.imavis.2014.08.004
A kernel-based framework for medical big-data analytics
Windridge, D. and Bober, M. 2014. A kernel-based framework for medical big-data analytics. in: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges Springer. pp. 197-208
Verification of concurrent quantum protocols by equivalence checking
Ardeshir-Larijani, E., Gay, S. and Nagarajan, R. 2014. Verification of concurrent quantum protocols by equivalence checking. TACAS 2014: 20th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. Grenoble, France 05 - 13 Apr 2014 Springer. https://doi.org/10.1007/978-3-642-54862-8_42
Model checking for communicating quantum processes
Davidson, T., Gay, S., Mlnařík, H., Nagarajan, R. and Papanikolaou, N. 2012. Model checking for communicating quantum processes. International Journal of Unconventional Computing. 8 (1), pp. 73-98.
Lossless quantum prefix compression for communication channels that are always open
Müller, M., Rogers, C. and Nagarajan, R. 2009. Lossless quantum prefix compression for communication channels that are always open. Physical Review A. 79 (1). https://doi.org/10.1103/PhysRevA.79.012302
Equivalence checking of quantum protocols
Ardeshir-Larijani, E., Gay, S. and Nagarajan, R. 2013. Equivalence checking of quantum protocols. in: Piterman, N. and Smolka, S. (ed.) Tools and Algorithms for the Construction and Analysis of Systems : 19th International Conference Proceedings (TACAS 2013). Berlin Springer.
QMC: a model checker for quantum systems
Gay, S., Nagarajan, R. and Papanikolaou, N. 2008. QMC: a model checker for quantum systems. in: Gupta, A. and Malik, S. (ed.) Computer Aided Verification : 20th International Conference, (CAV 2008) Proceedings Berlin Springer.
High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP)
Abbey, C., Hope, C., Sterr, A., Elangovan, P., Geades, N., Windridge, D., Young, K., Wells, K. and Mello-Thoms, C. 2013. High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP). SPIE Proceedings Vol. 8673. https://doi.org/10.1117/12.2007557
Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene
Taya, S., Windridge, D. and Osman, M. 2012. Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene. PLoS ONE. 7 (6). https://doi.org/10.1371/journal.pone.0039060
Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution
Poh, N., Windridge, D., Mottl, V., Tatarchuk, A. and Eliseyev, A. 2010. Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution. IEEE Transactions on Information Forensics and Security. 5 (3), pp. 461-469. https://doi.org/10.1109/TIFS.2010.2053535