Quantum Classification Algorithm Based on Competitive Learning Neural Network and Entanglement Measure
Source abstract
Abstract
In this paper, we develop a novel classification algorithm that is based on the integration between competitive learning and the computational power of quantum computing. The proposed algorithm classifies an input into one of two binary classes even if the input pattern is incomplete. We use the entanglement measure after applying unitary operators to conduct the competition between neurons in order to find the winning class based on wining-take-all. The novelty of the proposed algorithm is shown in its application to the quantum computer. Our idea is validated via classifying the state of Reactor Coolant Pump of a Risky Nuclear Power Plant and compared with other quantum-based competitive neural networks model.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Quantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Neural Networks and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center for Photonics and Smart Materials (CPSM), Zewail City of Science and Technology, October Gardens, 6th of October City, Giza 12578, Egypt
University of Science and Technology, Zewail City of Science and Technology, October Gardens, 6th of October City, Giza 12578, Egypt
Department of Physics, College of Sciences, University of Bisha, Bisha 61922, P.O. Box 344, Saudi Arabia
Physics Department, Faculty of Science, Al-Azhar University, Assiut 71524, Egypt
Department of Nuclear and Radiation Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
Deanship of Research and Graduate Studies, Applied Science University, P.O. Box 5055, 55222 Manama, Kingdom of Bahrain
Institute for Quantum Science and Engineering, Texas A&M University, College Station, TX 77843, USA
Department of Applied Sciences and Mathematics, College of Arts and Sciences, Abu Dhabi University, 59911 Abu Dhabi, United Arab Emirates
Department of Mathmatics, Sohage University, Sohag 82524, Egypt
References (62)
Show source-provided references
1. Nielsen, M.A., and Chuang, I.L. (2000). Quantum Computation and Quantum Information, Cambridge University Press.
2. Hagan, M., Demuth, H., and Beale, M. (1996). Neural Network Design, PWS publishing Company.
3. Simulating Physics with Computers10.1007/BF02650179 ↗
4. Coherent control of Atomic Excitation using Off-resonant Strong Few-cycle Pulses10.1103/PhysRevA.82.045805 ↗
5. Non-classical effects in cavity QED containing a nonlinear optical medium and a quantum well: Entanglement and non-Gaussanity10.1140/epjd/e2015-60198-0 ↗
6. Using Quantum Coherence to Generate Gain in the XUV and X-Ray: Gain-Swept Superradiance and Lasing Without Inversion10.1109/JSTQE.2011.2135339 ↗
7. Semiconductor cavity QED with squeezed light: Nonlinear regime10.1103/PhysRevA.84.053817 ↗
8. Beam splitting and entanglement generation: Excited coherent states10.1007/s11128-011-0344-9 ↗
9. Quantum Neural Computing10.1016/S1076-5670(08)70147-2 ↗
10. Quantum Associative Memory10.1016/S0020-0255(99)00101-2 ↗
11. Probabilistic quantum memories
12. Quantum Neural Networks10.1006/jcss.2001.1769 ↗
13. De Paula Neto, F.M., Ludermir, T.B., de Oliveira, W.R., and da Silva, A.J. (2018, January 8–13). Quantum Perceptron with Dynamic Internal Memory. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.10.1109/IJCNN.2018.8489170 ↗
14. Quantum Neural Networks for Face Recognition Classifier10.1016/j.proeng.2011.08.244 ↗
15. A quantum model for autonomous learning automata10.1007/s11128-013-0723-5 ↗
16. Quantum Speedup for Active Learning Agents10.1103/PhysRevX.4.031002 ↗
17. The quest for a quantum Neural Network10.1007/s11128-014-0809-8 ↗
18. Simulating a perceptron on a quantum computer10.1016/j.physleta.2014.11.061 ↗
19. Quantum perceptron over a field and neural network architecture seclection in a quantum computer10.1016/j.neunet.2016.01.002 ↗
20. Zhou, R., Cao, Y., Yang, S., and Xu, X. (2007, January 24–27). Quantum Storage Network. Proceedings of the 3rd International Conference on Natural Computation (ICNC), Haikou, Hainan, China.10.1109/ICNC.2007.592 ↗
21. Quantum Competitive Neural Network10.1007/s10773-009-0183-y ↗
22. Altaisky, M.V. (2001, July 05). Quantum Neural Networks. Available online: https://arxiv.org/abs/quant-ph/0107012.
23. Ventura, D. (1999, January 10–16). Implementing competitive learning in a quantum system. Proceedings of the International Joint Conference on Neural Networks (IJCNN’99), Washington, DC, USA.
24. A study of quantum neural networks
25. Quantum perceptron network
26. Neural network10.1007/s10773-007-9437-8 ↗
27. Model and algorithm of Quantum-inspired Neural Network with Sequence Input based on Controlled Rotation Gates10.1007/s10489-013-0447-3 ↗
28. Quantum-inspired neural network with quantum weights and real Weights10.4236/ojapps.2015.510060 ↗
29. Quantum-inspired neural networks with applications
30. Quantum-Inspired Neural Network with Sequence Input10.4236/ojapps.2015.56027 ↗
31. Mahajan, R.P. (2011, January 13–16). Hybrid Quantum Inspired Neural Model for Commodity Price Prediction. Proceedings of the ICACT, Seoul, Korea.
32. A Quantum Backpropagation Multilayer Perceptron (QBMLP) for Predicting Iron Adsorption Capacity of Calcareous Soil from Aqueous Solution10.1016/j.asoc.2014.11.019 ↗
33. Sagheer, A., and Zidan, M. (2013). Autonomous Quantum Perceptron Neural Network. arXiv, Available online: https://arxiv.org/abs/1312.4149.
34. Low-Cost Autonomous Perceptron Neural Network Inspired by Quantum Computation10.1063/1.5012145 ↗
35. A hybrid quantum-inspired neural networks with sequence inputs10.1016/j.neucom.2013.01.029 ↗
36. Xiao, H., and Cao, M. (2009, January 14–16). Hybrid Quantum Neural Networks Model Algorithm and Simulation. Proceedings of the 5th International Conference on Natural Computation, Tianjin, China.10.1109/ICNC.2009.128 ↗
37. Learning Algorithm and Application of Quantum BP Neural Networks based on universal quantum gates10.1016/S1004-4132(08)60063-8 ↗
38. Zidan, M., Sagheer, A., and Metwally, N. (2015, January 12–17). An Autonomous Competitive Learning Algorithm using Quantum Hamming Neural Networks. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland.10.1109/IJCNN.2015.7280719 ↗
39. Rojas, R. (1996). Unsupervised Learning and Clustering Algorithms, Neural Networks, Springer-Verlag.10.1007/978-3-642-61068-4_5 ↗
40. Quantum Competition Network Model Based On Quantum Entanglement10.4304/jcp.7.9.2312-2317 ↗
41. Quantum Mechanics Helps in Searching for a Needle in a Haystack10.1103/PhysRevLett.79.325 ↗
42. Sagheer, A., and Metwally, N. (2010, January 15–17). Communication via Quantum Neural Networks. Proceedings of the 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC, Fukuoka, Japan.10.1109/NABIC.2010.5716339 ↗
43. Geometric quantum discord under noisy environment10.1007/s11128-016-1261-8 ↗
44. Non-local correlation and quantum discord in two atoms in the non-degenerate model10.1016/j.aop.2012.08.003 ↗
45. Using measurement-induced disturbance to characterize correlations as classical or quantum10.1103/PhysRevA.77.022301 ↗
46. Quantum discord: A measure of the quantumness of corre- lations10.1103/PhysRevLett.88.017901 ↗
47. Geometric measure of quantum discord10.1103/PhysRevA.82.034302 ↗
48. Dynamical behavior of entanglement in semiconductor microcavities10.1016/j.physe.2010.03.032 ↗
49. Entanglement of a Pair of Quantum Bits10.1103/PhysRevLett.78.5022 ↗
50. Entanglement of Formation of an Arbitrary State of Two Qubits10.1103/PhysRevLett.80.2245 ↗
51. Zhou, L., and Sheng, Y.B. (2015). Concurrence Measurement for the Two-Qubit Optical and Atomic States. Entropy, 17.10.3390/e17064293 ↗
52. Computable measure of entanglement10.1103/PhysRevA.65.032314 ↗
53. Measuring entanglement entropy in a quantum many-body system10.1038/nature15750 ↗
54. Direct measurement of concurrence for atomic two-qubit pure states10.1103/PhysRevA.75.032303 ↗
55. Experimental determination of entanglement with a single measurement10.1038/nature04627 ↗
56. A Novel Algorithm based on Entanglement Measurement for Improving Speed of Quantum Algorithms10.18576/amis/120127 ↗
57. El-Wazan, K., Younes, A., and Doma, S.B. (arXiv, 2017). A Quantum Algorithm for Testing Junta Variables and Learning Boolean Functions via Entanglement Measure, arXiv.
58. El-Wazan, K. (arXiv, 2019). A Measuring Hamming Distance between Boolean Functions via Entanglement Measure, arXiv.
59. Application of quantum algorithms to direct measurement of concurrence of a two-qubit pure state10.1088/1674-1056/18/7/004 ↗
60. A Single Quantum Cannot be Cloned10.1038/299802a0 ↗
61. Application of neural networks to multiple alarm processing and diagnosis in nuclear power plants10.1109/23.199482 ↗
62. Applications of fault detection and diagnosis methods in nuclear power plants: A review10.1016/j.pnucene.2010.12.001 ↗