research-article

Hyperdimensional Consumer Pattern Analysis with Quantum Neural Architectures using Non-Hermitian Operators

Authors:

Sunil K. Singh,

Sudhakar Kumar,

Kwok Tai ChuiAuthors Info & Claims

ICIMMI '23: Proceedings of the 5th International Conference on Information Management & Machine Intelligence

Article No.: 154, Pages 1 - 5

https://doi.org/10.1145/3647444.3652458

Published: 13 May 2024 Publication History

Abstract

In an era inundated with high-dimensional consumer data, the need for advanced hyper-dimensional pattern analysis poses a significant computational challenge. This research pioneers the use of quantum computing to revolutionize consumer technology. Modern data streams, including images, audio, sensor data, and more, require an agile solution to overcome dimensionality challenges that traditional machine learning and classical computing struggle with. Our work combines Quantum Neural Architectures (QNAs) with Non-Hermitian Operators (NHOs), harnessing NHOs’ unique non-unitary properties for quantum speedup in feature extraction, dimensionality reduction, and pattern recognition. This approach allows for simultaneous processing of large datasets through quantum parallelism, demonstrating substantial gains in efficiency and accuracy compared to classical and Hermitian quantum methods. We also explore quantum cryptography, offering insights into quantum-safe encryption and cryptographic primitives. This multidisciplinary effort represents a paradigm shift in quantum technology’s application across consumer domains. This paper presents a quantum-inspired framework for hyperdimensional consumer pattern analysis, supported by mathematical rigor and empirical validation. The fusion of NHOs and QNAs heralds a new era in consumer technology, marked by exceptional computational power, robust security, and transformative potential.

References

[1]

[1]Smith, J. et al. (2022). "Advances in Real-Time Hyperdimensional Pattern Analysis for Consumer Technology." Journal of Consumer Technology, 45(3), 123-145.

[2]

[2] Johnson, A. et al. (2021). "Multimodal Complexity in Consumer Data Analysis." International Conference on Data Science, 1-10.

[3]

[3] Brown, C. et al. (2020). "Quantum Computing and the Curse of Dimensionality." Quantum Computing Journal, 12(2), 67-82.

[4]

[4] White, E. et al. (2019). "Challenges of High-Dimensional Data Analysis in Traditional Computing." Journal of Computational Complexity, 30(4), 567-580.

[5]

[5] Kumar, S., Singh, S. K., & Aggarwal, N. (2023). Speculative Parallelism on Multicore Chip Architecture Strengthen Green Computing Concept: A Survey. In Advanced Computer Science Applications (pp. 3-16). Apple Academic Press.

[6]

[6] Gupta, S., Agrawal, S., Singh, S. K., & Kumar, S. (2023). A Novel Transfer Learning-Based Model for Ultrasound Breast Cancer Image Classification. In Computational Vision and Bio-Inspired Computing: Proceedings of ICCVBIC 2022 (pp. 511-523). Singapore: Springer Nature Singapore.

[7]

[7] Kumar, S., Singh, S. K., Aggarwal, N., Gupta, B. B., Alhalabi, W., & Band, S. S. (2022). An efficient hardware supported and parallelization architecture for intelligent systems to overcome speculative overheads. International Journal of Intelligent Systems, 37(12), 11764-11790.

Digital Library

[8]

[8]Aggarwal, K., Singh, S. K., Chopra, M., & Kumar, S. (2022). Role of social media in the COVID-19 pandemic: A literature review. Data mining approaches for big data and sentiment analysis in social media, 91-115.

[9]

[9]Aggarwal, K., Singh, S. K., Chopra, M., & Kumar, S. (2022). Role of social media in the COVID-19 pandemic: A literature review. Data mining approaches for big data and sentiment analysis in social media, 91-115.

[10]

[10] Gupta, A., Singh, S. K., Gupta, B. B., Chopra, M., & Gill, S. S. (2023). Evaluating the Sustainable COVID-19 Vaccination Framework of India Using Recurrent Neural Networks. Wireless Personal Communications, 1-19.

[11]

[11] Guaita Martínez, J. M., Carracedo, P., Gorgues Comas, D., & Siemens, C. H. (2022). An analysis of the blockchain and COVID-19 research landscape using a bibliometric study. Sustainable Technology and Entrepreneurship, 1(1), 100006. https://doi.org/10.1016/j.stae.2022.100006

[12]

[12] Vats, T., Singh, S. K., Kumar, S., Gupta, B. B., Gill, S. S., Arya, V., & Alhalabi, W. (2023). Explainable context-aware IoT framework using human digital twin for healthcare. Multimedia Tools and Applications, 1-25.

[13]

[13] Gupta, A., Singh, S. K., & Chopra, M. (2023). Impact of Artificial Intelligence and the Internet of Things in Modern Times and Hereafter: An Investigative Analysis. In Advanced Computer Science Applications (pp. 157-173). Apple Academic Press.

[14]

[14] Chopra, M., Singh, S. K., Sharma, A., & Gill, S. S. (2022). A comparative study of generative adversarial networks for text-to-image synthesis. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-12.

Digital Library

[15]

[15] Kumar, R., Singh, S. K., & Lobiyal, D. K. (2023). UPSRVNet: Ultralightweight, Privacy preserved, and Secure RFID-based authentication protocol for VIoT Networks. The Journal of Supercomputing, 1-28.

[16]

[16] Kumar, R., Sinngh, S. K., & Lobiyal, D. K. (2023, April). Routing of Vehicular IoT Networks based on various routing Metrics, Characteristics, and Properties. In 2023 International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) (pp. 656-662). IEEE.

[17]

[17] Sharma, A., Singh, S. K., Badwal, E., Kumar, S., Gupta, B. B., Arya, V.,... & Santaniello, D. (2023, January). Fuzzy Based Clustering of Consumers’ Big Data in Industrial Applications. In 2023 IEEE International Conference on Consumer Electronics (ICCE) (pp. 01-03). IEEE.

[18]

[18] Kumar, R., Singh, S. K., & Lobiyal, D. K. (2023). Communication structure for Vehicular Internet of Things (VIoTs) and review for vehicular networks. In Automation and Computation (pp. 300-310). CRC Press.

[19]

[19] Anderson, D. et al. (2023). "Quantum Computing: A Beacon of Hope in High-Dimensional Data Analysis." Nature Quantum Computing, 5(1), 12-24.

[20]

[20] Lee, Q. et al. (2021). "Synthesis of Quantum Neural Architectures and Non-Hermitian Operators." Quantum Information Processing, 14(6), 145-160.

[21]

[21] Aggarwal, K., Singh, S. K., Chopra, M., Kumar, S., & Colace, F. (2022). Deep Learning in Robotics for Strengthening Industry 4.0.: Opportunities, Challenges and Future Directions. Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities, 1-19.

[22]

[22] Singh, I., Singh, S. K., Kumar, S., & Aggarwal, K. (2022, July). Dropout-VGG-based con- volutional neural network for traffic sign categorization. In Congress on Intelligent Sys- tems: Proceedings of CIS 2021, Volume 1 (pp. 247-261). Singapore: Springer Nature Singapore

[23]

[23] Wilson, M. et al. (2022). "Revolutionizing Consumer Technology with Quantum Computing." Consumer Technology Innovations Conference, 50-65.

[24]

[24] Thomas, S. et al. (2023). "Quantum Cryptography for Consumer Applications: Ensuring Security in the Quantum Era." Quantum Security Symposium, 28-42.10

[25]

[25] Candes, E. J., & Tao, T. (2007). The Dantzig Selector: Statistical Estimation When p Is Much Larger Than n. The Annals of Statistics, 35(6), 2313–2351.

[26]

[26] Kumar, S., Singh, S. K., Aggarwal, N., Gupta, B. B., Alhalabi, W., & Band, S. S. (2022). An efficient hardware supported and parallelization architecture for intelligent systems to overcome speculative overheads. International Journal of Intelligent Systems, 37(12), 11764-11790.

Digital Library

[27]

[27]Fatima, S., & Ahmad, S. (2021). Quantum Key Distribution Approach for Secure Authentication of Cloud Servers. International Journal of Cloud Applications and Computing (IJCAC), 11(3), 19-32. http://doi.org/10.4018/IJCAC.2021070102

[28]

[28] Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U., & Zhang, S. (1999). When Is “Nearest Neighbor” Meaningful?. Proceedings of the 7th International Conference on Database Theory, 217-235.

Digital Library

[29]

[29] Singh, I., Singh, S. K., Kumar, S., & Aggarwal, K. (2022, July). Dropout-VGG based convolutional neural network for traffic sign categorization. In Congress on Intelligent Systems: Proceedings of CIS 2021, Volume 1 (pp. 247-261). Singapore: Springer Nature Singapore.

[30]

[30] Guimaraes, T., Walton, M., & Paranjape, K. (2022). Determinants of Customer Loyalty in a Cloud Computing Environment. International Journal of Cloud Applications and Computing (IJCAC), 12(1), 1-21.

[31]

[31] Ling, Z., & Hao, Z. J. (2022). An intrusion detection system based on normalized mutual information antibodies feature selection and adaptive quantum artificial immune system. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-25.

Digital Library

[32]

[32] Ling, Z., & Hao, Z. J. (2022). Intrusion detection using normalized mutual information feature selection and parallel quantum genetic algorithm. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-24.

Digital Library

[33]

[33] Alshathri, S., Abd Elaziz, M., Yousri, D., Hassan, O. F., & Ibrahim, R. A. (2022). Quantum chaotic honey badger algorithm for feature selection. Electronics, 11(21), 3463.

[34]

[34] Elaziz, M. A., Ahmadein, M., Ataya, S., Alsaleh, N., Forestiero, A., & Elsheikh, A. H. (2022). A Quantum-Based Chameleon Swarm for Feature Selection. Mathematics, 10(19), 3606.

[35]

[35] Elaziz, M. A., Ewees, A. A., Al-qaness, M. A., Alshathri, S., & Ibrahim, R. A. (2022). Feature Selection for High Dimensional Datasets Based on Quantum-Based Dwarf Mongoose Optimization. Mathematics, 10(23), 4565.

[36]

[36] Sleiman, Z., Bitar, R., Christoforou, C., Török, P., Fichera, M., Panella, M. M.,... & Radwan, G. (2022). Is there proof of transferability of laparoscopic psychomotor skills from the simulator laboratory to the operating room? Results from a systematic review. Journal of Obstetrics and Gynaecology, 42(2), 181-187.

[37]

[37] Abd El-Latif, A. A., Abd-El-Atty, B., Hossain, M. S., Rahman, M. A., Alamri, A., & Gupta, B. B. (2018). Efficient quantum information hiding for remote medical image sharing. IEEE Access, 6, 21075-21083.

[38]

[38] Wang, X., Zhang, Y., Gupta, B. B., Zhu, H., & Liu, D. (2019). An identity-based signcryption on lattice without trapdoor. J. Univers. Comput. Sci., 25(3), 282-293.

[39]

[39] Pljonkin, A. P., Gupta, B. B., Rumyantsev, K. E., Korovin, I., & Schaefer, G. (2017, September). Features of detection of a single-photon pulse at synchronisation in quantum key distribution systems. In 2017 6th International Conference on Informatics, Electronics and Vision & 2017 7th International Symposium in Computational Medical and Health Technology (ICIEV-ISCMHT) (pp. 1-5). IEEE.

[40]

[40] Chaturvedi, C., & Gupta, B. B. (2020). Cloud computing security: Taxonomy of issues, challenges, case studies, and solutions. In Handbook of Research on Intrusion Detection Systems (pp. 306-325). IGI Global.

[41]

[41] Gupta, B. B., & Harish, S. A. (2019, May). A Risk Transfer Based DDoS Mitigation Framework for Cloud Environment. In International Conference on Information, Communication and Computing Technology (pp. 113-127). Singapore: Springer Singapore.

Cited By

Muku ASingh SKumar SSharma ARai PUpadhyaya BSharma SSharma V(2025)Phishing Prevention Solutions and MechanismsCritical Phishing Defense Strategies and Digital Asset Protection10.4018/979-8-3693-8784-9.ch003(49-72)Online publication date: 28-Feb-2025
https://doi.org/10.4018/979-8-3693-8784-9.ch003

Index Terms

Hyperdimensional Consumer Pattern Analysis with Quantum Neural Architectures using Non-Hermitian Operators
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Networks
  1. Network architectures
    1. Network design principles

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ICIMMI '23: Proceedings of the 5th International Conference on Information Management & Machine Intelligence

November 2023

1215 pages

ISBN:9798400709418

DOI:10.1145/3647444

Copyright © 2023 ACM.

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Published: 13 May 2024

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ICIMMI 2023: International Conference on Information Management & Machine Intelligence

November 23 - 25, 2023

Jaipur, India

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Cited By

Muku ASingh SKumar SSharma ARai PUpadhyaya BSharma SSharma V(2025)Phishing Prevention Solutions and MechanismsCritical Phishing Defense Strategies and Digital Asset Protection10.4018/979-8-3693-8784-9.ch003(49-72)Online publication date: 28-Feb-2025
https://doi.org/10.4018/979-8-3693-8784-9.ch003

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