Publication: Fraud detection with a single-qubit quantum neural network
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2022
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This paper presents, via an explicit real-world example, a hands-on introduction to the field of quantum machine learning. We focus on the case of learning with a single qubit, using data re-uploading techniques. After a discussion of the relevant background in quantum computing and machine learning, and an overview of state of the art methods in QML, we provide a thorough explanation of the data re-uploading models that we consider, and implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK. Interestingly, the results show that single-qubit classifiers can achieve a performance that is on-par with classical counterparts under the same set of training conditions. While this cannot be understood as a proof of the advantage of quantum machine learning, it points to a promising research direction, and raises a series of questions that we outline.
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[1] M. Schuld and F. Petruccione. Supervised learning with quantum computers, volume 17. Springer, 2018.
[2] B. Denby, M. Campbell, F. Bedeschi, N. Chriss, C. Bowers, and F. Nesti. Neural networks for triggering. IEEE Transactions on Nuclear Science, 37(2):248{254, 1990.
[3] P. Baldi, P. Sadowski, and D. Whiteson. Searching for exotic particles in high-energy physics with deep learning. Nature Commmunications, 5:4308, 2014.
[4] P. Abiuso, T. Kriváchy, E.-C. Boghiu, M.-O. Renou, A. Pozas-Kerstjens, and A. Acín. Singlephoton nonlocality in quantum networks. Physical Review Research, 4:L012041, Mar 2022.
[5] E. M. Stoudenmire and D. J. Schwab. Supervised learning with tensor networks. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 29, pages 4799-4807. Curran Associates, Inc., 2016.
[6] J. M. Arrazola, A. Delgado, B. R. Bardhan, and S. Lloyd. Quantum-inspired algorithms in practice. Quantum, 4:307, August 2020.
[7] T. Felser, M. Trenti, L. Sestini, A. Gianelle, D. Zuliani, D. Lucchesi, and S. Montagnero. Quantuminspired machine learning on high-energy physics data. npj Quantum Information, 7:111, 2021.
[8] A. Pozas-Kerstjens, S. Hernández-Santana, J. R. Pareja Monturiol, M. Castrillón López, G. Scarpa, C. E. González-Guillén, and D. Pérez-García. Physics solutions for machine learning privacy leaks. arXiv:2202.12319, 2022.
[9] S. Arunachalam and R. de Wolf. Guest column: A survey of quantum learning theory. ACM SIGACT News, 48(2):41-67, 2017.
[10] J. Preskill. Quantum computing in the nisq era and beyond. Quantum, 2:79, 2018.
[11] A. Pérez-Salinas, A. Cervera-Lierta, E. Gil-Fuster, and J. I. Latorre. Data re-uploading for a universal quantum classifier. Quantum, 4:226, 2020.
[12] A. Pérez-Salinas, D. López-Nuñez, A. García-Sáez, P. Forn-Díaz, and J. I. Latorre. One qubit as a universal approximant. Physical Review A, 104(1):012405, 2021.
[13] Qiskit: An open-source framework for quantum computing. www.qiskit.org, 2021.
[14] Kaggle credit card fraud detection dataset. https://www.kaggle.com/mlg-ulb/creditcardfraud. Accessed: 2021-08-15.
[15] T. Dutta, A. Pérez-Salinas, J. P. S. Cheng, J. I. Latorre, and M. Mukherjee. Single-qubit universal classifier implemented on an ion-trap quantum device. Physical Review A, 106:012411, Jul 2022.
[16] O. Kyriienko and E. B. Magnusson. Unsupervised quantum machine learning for fraud detection. arXiv:2208.01203, 2022.
[17] M. A. Nielsen and I. Chuang. Quantum computation and quantum information. Cambridge University Press, 2002.
[18] M. A. Nielsen. Neural networks and deep learning, volume 25. Determination press San Francisco, CA, 2015.
[19] F. V. Massoli, L. Vadicamo, G. Amato, and F. Falchi. A leap among quantum computing and quantum neural networks: A survey. ACM Computing Surveys, apr 2022.
[20] IBM cloud education, What is Machine Learning? https://www.ibm.com/cloud/learn/machine-learning. Accessed: 2021-10-10.
[21] G. Cybenko. Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4):303-314, 1989.
[22] K. Hornik, M. Stinchcombe, and H. White. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5):359-366, 1989.
[23] www.python.org.
[24] F. Chollet et al. Keras. https://keras.io, 2015.