Quantum error-correcting output codes

Article

Windridge, D., Mengoni, R. and Nagarajan, R. 2018. Quantum error-correcting output codes. International Journal of Quantum Information. 16 (8). https://doi.org/10.1142/S0219749918400038
TypeArticle
TitleQuantum error-correcting output codes
AuthorsWindridge, D., Mengoni, R. and Nagarajan, R.
Abstract

Quantum machine learning is the aspect of quantum computing concerned with the design of algorithms capable of generalized learning from labeled training data by effectively exploiting quantum effects. Error-correcting output codes (ECOC) are a standard setting in machine learning for efficiently rendering the collective outputs of a binary classifier, such as the support vector machine, as a multi-class decision procedure. Appropriate choice of error-correcting codes further enables incorrect individual classification decisions to be effectively corrected in the composite output. In this paper, we propose an appropriate quantization of the ECOC process, based on the quantum support vector machine. We will show that, in addition to the usual benefits of quantizing machine learning, this technique leads to an exponential reduction in the number of logic gates required for effective correction of classification error.

KeywordsQuantum machine learning; error-correcting output codes; support vector machines
Research GroupFoundations of Computing group
PublisherWorld Scientific Publishing Co. Pte Ltd
JournalInternational Journal of Quantum Information
ISSN0219-7499
Electronic1793-6918
Publication dates
Online15 Aug 2018
Print01 Dec 2018
Publication process dates
Deposited29 Aug 2018
Accepted24 Jun 2018
Output statusPublished
Accepted author manuscript
Copyright Statement

Electronic version of an article published as International Journal of Quantum Information, Vol. 16, No. 08, 2018, Article DOI: https://doi.org/10.1142/S0219749918400038 © 2018 World Scientific Publishing Company. Journal URL: https://www.worldscientific.com/worldscinet/ijqi

Digital Object Identifier (DOI)https://doi.org/10.1142/S0219749918400038
Scopus EID2-s2.0-85052966708
Web of Science identifierWOS:000453520000004
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Has metadatahttp://www.scopus.com/inward/record.url?eid=2-s2.0-85052966708&partnerID=MN8TOARS
LanguageEnglish
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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.
Trained eyes: experience promotes adaptive gaze control in dynamic and uncertain visual environments
Taya, S., Windridge, D. and Osman, M. 2013. Trained eyes: experience promotes adaptive gaze control in dynamic and uncertain visual environments. PLoS ONE. 8 (8). https://doi.org/10.1371/journal.pone.0071371
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