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International Journal of Computer Science Issues

Towards quantum agents: the superposition state property

Published Sep 30, 2016• 4 citations
Publisher

Abstract

Recommender systems are an Artificial Intelligence technology that has become an essential part of business for many industries and businesses.Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user.The system learns from a customer and recommends products that she will find most valuable from among the available products.They serve many types of E-commerce applications, from direct product recommendation for an individual to helping someone find a gift for a third party [10].Recently the world of the web has become more social and more real-time.Facebook and Twitter [16] are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research.In this research project, the researcher tries to provide a present an analysis of how recommender systems can be used in E-commerce today, companies, SMEs, and other social institutions to improve sales, maintain good customer relationship and loyalty, and saves customers sourcing time.Recommender Systems too can also be used to analyze Big Data, although there are some key challenges associated with Big Data analytics.The impact of data abundance extends well beyond business [15].But the computer tools for gleaning knowledge and insights from the Internet era's vast trove of unstructured data are fast gaining ground.At the forefront are the rapidly advancing techniques of artificial intelligence like natural-language processing, pattern recognition and machine learning.The researcher hopes that these key problems with improved recommender systems in the future: hybrid data, predictable recommendations, scalability, and incorporation of content, such problems could be resolved.If recommender systems are able to surmount these challenges, they have the potential to become an essential component of doing business in Ecommerce

DOI
10.20943/01201605.2027

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Bajwa, "Integration of quantum information systems in computer science and in electrical engineering," J. Integr. Des. Process Sci., vol. 19, no. 4, pp. 9-20, 2016. [20] A. JavadiAbhari et al., "ScaffCC: A framework for compilation and analysis of quantum computing programs," in Proc. 11th ACM Conf. Comput. Front., 2014, pp. 1-10. [21] S. Khanra, A. Dhir, A. N. Islam, and M. Mäntymäki, "Big data analytics in healthcare: A systematic literature review," Enterp. Inf. Syst., vol. 14, no. 7, pp. 878-912, 2020. [22] S. Ahmed, I. Ahmed, M. Kamruzzaman, and R. Saha, "Cybersecurity challenges in IT infrastructure and data management: A comprehensive review of threats, mitigation strategies, and future trend," Glob. Mainstream J. Innov. Eng. Emerg. Technol., vol. 1, no. 1, pp. 36-61, 2022. [23] O. M. Araz, T. M. Choi, D. L. Olson, and F. S. Salman, "Role of analytics for operational risk management in the era of big data," Decis. Sci., vol. 51, no. 6, pp. 1320-1346, 2020. [24] R. Orús, S. 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Recommender systems are an Artificial Intelligence technology that has become an essential part of business for many industries and businesses.Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user.The system learns from a customer and recommends products that she will find most valuable from among the available products.They serve many types of E-commerce applications, from direct product recommendation for an individual to helping someone find a gift for a third party [10].Recently the world of the web has become more social and more real-time.Facebook and Twitter [16] are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research.In this research project, the researcher tries to provide a present an analysis of how recommender systems can be used in E-commerce today, companies, SMEs, and other social institutions to improve sales, maintain good customer relationship and loyalty, and saves customers sourcing time.Recommender Systems too can also be used to analyze Big Data, although there are some key challenges associated with Big Data analytics.The impact of data abundance extends well beyond business [15].But the computer tools for gleaning knowledge and insights from the Internet era's vast trove of unstructured data are fast gaining ground.At the forefront are the rapidly advancing techniques of artificial intelligence like natural-language processing, pattern recognition and machine learning.The researcher hopes that these key problems with improved recommender systems in the future: hybrid data, predictable recommendations, scalability, and incorporation of content, such problems could be resolved.If recommender systems are able to surmount these challenges, they have the potential to become an essential component of doing business in Ecommerce
Ecommerce has matured into a data‑intensive ecosystem where consumer behavior, logistics, pricing, personalization, and fraud detection are driven by large, heterogeneous datasets. Classical AI and data‑science pipelines have delivered major productivity and revenue gains, yet they face limits in modeling combinatorial recommendation spaces, accelerating molecular‑scale cryptography for secure transactions, and solving certain optimization problems at scale. Quantum Neural Networks (QNNs) and hybrid quantum‑classical approaches promise novel representational capacity and computational primitives that can enrich recommender systems, optimization for supply chains, privacy‑preserving analytics, and next‑generation fraud detection. This article synthesizes theory and practice across AI, data science, and QNNs for e‑commerce. It offers: (1) a conceptual framework linking data modalities and business problems; (2) detailed methods for modeling, training, deployment, and evaluation; (3) extended technical sections on quantum representations, QNN architectures, and hybrid pipelines with reproducible pseudocode; (4) applied case studies (recommendation, dynamic pricing, inventory optimization, personalization, fraud detection, privacy); (5) engineering and MLOps considerations; (6) an ethics, privacy, and regulatory analysis; and (7) a prioritized research and deployment roadmap. The paper targets researchers and practitioners aiming to integrate quantum‑enhanced AI into production e‑commerce systems while preserving interpretability, fairness, security, and economic value.
Massive amounts of data in many forms, including text, images, video, and more, are referred to as big data. The storage and processing capabilities of a conventional computer can’t keep up with the demands of big data. As a result, it’s tough to spot patterns in large amounts of data. Large quantities of data take a typical computer a long time to process. Using quantum computing, data and information may be calculated quickly. Using big data with quantum machine learning to detect data patterns for a range of applications is the subject of this review research. When compared to traditional computers, quantum-guided and unsupervised machine learning have distinct benefits. The challenges, future scope, and techniques of quantum computing with machine learning algorithms are also addressed. Some real-world applications have been suggested, even if quantum machine learning is still an emerging field. This means that machine learning is being considered for industrial applications, and several studies have cited machine learning as one of the key enablers to move traditional production systems up to today’s high-tech “Industry 4.0” standards. Professionals in this sector will be able to gain a better understanding of the subject matter through the presentation and organization of this complete review.
There is a major promise of extensive technical disturbance made by increasing convergence of two rapid development areas: Quantum Computing & Artificial Intelligence (AI). Unlike traditional computers, quantum computer use quantum -mechanical concepts as complication & superposition towards process data in a new way. Because of this, quantum computer machines can improve traditional systems towards solve certain types of problems. Machine learning (ML) algorithm is a sub -region of artificial intelligence aimed at automatically analyzing, interpreting & predicting procedures on a dataset. It is expected that the merger of AI & quantum calculation can quickly increase the processor power available for machine learning, which will accelerate processes such as data analysis, mimic quantum systems & handle complex customization problems. Medication development, material research & encryption abide some of the areas that benefit from the correction of quantum calculation of AI. among Quantum-Enhanced AI, the training machine learning models can endure very quick & more effective, so we can take problems that were impossible in the past. Pattern recognition, classification & decision -making artificial intelligence abide corners of applications; Quanta algorithms can also provide better ways for these features. Nevertheless, there abide still obstacles towards removing, such as creating special quantum algorithms for AI functions, ensuring that quantum systems can fix incorrectly, & make quantum machine scalable. Despite these challenges, the combination of AI & quantum calculation has the opportunity towards enter a new era of technical progress, which has not been able towards solve traditional data processing by answering major, complex problems so far.
Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain.
Over the past decade, the field of Classical Machine Learning has experienced rapid growth, primarily driven by the widespread adoption of the internet and the increasing interconnectivity of digital devices.This expansion has led to the generation of vast volumes of data requiring effective analysis and processing.Machine Learning, as a subfield of Artificial Intelligence, leverages tools mainly from Statistics to identify patterns and extract meaningful insights from data without the need for explicit programming.At the same time, Quantum Computing introduces a fundamentally different paradigm for information processing, utilizing principles of Quantum Mechanics, such as superposition and entanglement.The convergence of these two scientific domains has given rise to the emerging field of Quantum Machine Learning.The present thesis aims to investigate whether the integration of Machine Learning and Quantum Computing can offer tangible advancements in the efficiency and performance of Artificial Intelligence systems.Based on current research findings, this study explores the potential, advantages, and limitations of this interdisciplinary approach, as well as its prospects for development in the coming years.
The rapid growth of Big Data has pushed classical computing systems to their limits, necessitating innovative approaches to data processing. Quantum computing, with its inherent parallelism and entanglement capabilities, offers transformative potential for solving complex, large-scale problems that are intractable for classical systems. This review paper examines the role of quantum computing in future Big Data processing, analyzing key quantum algorithms such as Grover's search, Shor's factorization, and quantum machine learning techniques. We explore fundamental concepts including qubit operations, quantum complexity classes, and hybrid quantum-classical architectures, while critically assessing current hardware limitations like de-coherence and error rates in NISQ-era devices. The paper highlights promising applications in optimization, secure communication, and high-dimensional data analysis, alongside significant challenges in data encoding, algorithmic readiness, and practical implementation. Emerging research directions are discussed, including near-term NISQ applications, fault-tolerant quantum computing prospects, and cross-disciplinary opportunities in NLP and IoT. By synthesizing theoretical advances with practical constraints, this review provides a balanced perspective on quantum computing's evolving role in Big Data, offering insights into both its revolutionary potential and the substantial barriers that must be overcome for widespread adoption. The findings suggest that while quantum advantage remains limited to specific use cases today, continued progress in hardware and algorithms may eventually redefine the landscape of large-scale data processing. References [1] S. S. Gill et al., "Quantum computing: A taxonomy, systematic review and future directions," Softw. Pract. Exper., vol. 52, no. 1, pp. 66-114, 2022. [2] R. Ur Rasool et al., "Quantum computing for healthcare: A review," Future Internet, vol. 15, no. 3, p. 94, 2023. [3] S. Zhu et al., "Intelligent computing: The latest advances, challenges, and future," Intell. Comput., vol. 2, p. 0006, 2023. [4] M. Möller and C. Vuik, "On the impact of quantum computing technology on future developments in high-performance scientific computing," Ethics Inf. Technol., vol. 19, pp. 253-269, 2017. [5] W. Zhang et al., "Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge," Gondwana Res., vol. 109, pp. 1-17, 2022. [6] S. D. Pasham, "Privacy-preserving data sharing in big data analytics: A distributed computing approach," Metascience, vol. 1, no. 1, pp. 149-184, 2023. [7] F. Bova, A. Goldfarb, and R. G. Melko, "Commercial applications of quantum computing," EPJ Quantum Technol., vol. 8, no. 1, p. 2, 2021. [8] U. Tariq et al., "A critical cybersecurity analysis and future research directions for the internet of things: A comprehensive review," Sensors, vol. 23, no. 8, p. 4117, 2023. [9] M. R. Haque et al., "The role of macroeconomic discourse in shaping inflation views: Measuring public trust in Federal Reserve policies," J. Bus. Insight Innov., vol. 2, no. 2, pp. 88-106, 2023. [10] M. A. Sayem et al., "AI-driven diagnostic tools: A survey of adoption and outcomes in global healthcare practices," Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 10, pp. 1109-1122, 2023. [11] M. Z. Afshar, "Exploring factors impacting organizational adaptation capacity of Punjab Agriculture & Meat Company (PAMCO)," Int. J. Emerg. Issues Soc. Sci. Arts Humanit., vol. 2, no. 1, pp. 1-10, 2023. [12] T. A. Shaikh and R. Ali, "Quantum computing in big data analytics: A survey," in Proc. IEEE Int. Conf. Comput. Inf. Technol. (CIT), 2016, pp. 112-115. [13] A. Pandey and V. Ramesh, "Quantum computing for big data analysis," Indian J. Sci., vol. 14, no. 43, pp. 98-104, 2015. [14] J. Singh and M. Singh, "Evolution in quantum computing," in Proc. Int. Conf. Syst. Model. Adv. Res. Trends (SMART), 2016, pp. 267-270. [15] C. P. Chen and C. Y. Zhang, "Data-intensive applications, challenges, techniques and technologies: A survey on big data," Inf. Sci., vol. 275, pp. 314-347, 2014. [16] X. D. Cai et al., "Entanglement-based machine learning on a quantum computer," Phys. Rev. Lett., vol. 114, no. 11, p. 110504, 2015. [17] D. P. Acharjya and K. Ahmed, "A survey on big data analytics: Challenges, open research issues and tools," Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 2, pp. 511-518, 2016. [18] S. Yu, "Big privacy: Challenges and opportunities of privacy study in the age of big data," IEEE Access, vol. 4, pp. 2751-2763, 2016. [19] O. N. Garcia and G. Bajwa, "Integration of quantum information systems in computer science and in electrical engineering," J. Integr. Des. Process Sci., vol. 19, no. 4, pp. 9-20, 2016. [20] A. JavadiAbhari et al., "ScaffCC: A framework for compilation and analysis of quantum computing programs," in Proc. 11th ACM Conf. Comput. Front., 2014, pp. 1-10. [21] S. Khanra, A. Dhir, A. N. Islam, and M. Mäntymäki, "Big data analytics in healthcare: A systematic literature review," Enterp. Inf. Syst., vol. 14, no. 7, pp. 878-912, 2020. [22] S. Ahmed, I. Ahmed, M. Kamruzzaman, and R. Saha, "Cybersecurity challenges in IT infrastructure and data management: A comprehensive review of threats, mitigation strategies, and future trend," Glob. Mainstream J. Innov. Eng. Emerg. Technol., vol. 1, no. 1, pp. 36-61, 2022. [23] O. M. Araz, T. M. Choi, D. L. Olson, and F. S. Salman, "Role of analytics for operational risk management in the era of big data," Decis. Sci., vol. 51, no. 6, pp. 1320-1346, 2020. [24] R. Orús, S. Mugel, and E. Lizaso, "Quantum computing for finance: Overview and prospects," Rev. Phys., vol. 4, p. 100028, 2019. [25] D. Herman et al., "A survey of quantum computing for finance," arXiv:2201.02773, 2022. [26] S. S. Gill et al., "AI for next generation computing: Emerging trends and future directions," Internet Things, vol. 19, p. 100514, 2022. [27] A. Deshmukh, D. S. Patil, P. D. Pawar, and S. Kumari, "Recent trends for smart environments with AI and IoT-based technologies: A comprehensive review," in Handbook of Research on Quantum Computing for Smart Environments, 2023, pp. 435-452. [28] S. J. Nawaz et al., "Quantum machine learning for 6G communication networks: State-of-the-art and vision for the future," IEEE Access, vol. 7, pp. 46317-46350, 2019. [29] M. Asif, "Integration of Information Technology in Financial Services and its Adoption by the Financial Sector in Pakistan," Inverge J. Soc. Sci., vol. 1, no. 2, pp. 23-35, 2022.