Quantum Computing: The Next Frontier in Computational Power

By: Brij B. Gupta, Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan. Email: bbgupta@asia.edu.tw


In the rapidly evolving field of computational technology, quantum computing emerges as the groundbreaking frontier poised to redefine the limits of processing power and problem-solving capabilities. This blog delves into the intricate world of quantum computing, juxtaposing it with traditional computing paradigms to unveil its revolutionary potential. Through a detailed exploration of quantum bits, superposition, entanglement, and the challenges that currently beset this nascent technology, we illuminate the path forward for quantum computing. From its applications in cryptography and drug discovery to its implications for artificial intelligence and data security, the blog offers a comprehensive overview of quantum computing’s current state, potential applications, and the ethical considerations it raises. As we stand on the brink of a new computational era, this piece not only aims to inform but also to spark discourse on the future of quantum computing and its role in shaping our digital and physical worlds.


Traditional computing, characterized by a central processing unit that processes data transferred from a separate memory unit via a data bus, faces several limitations. One major constraint is the bottleneck on data throughput due to the sequential nature of data transfer in traditional computing systems [1]. Additionally, traditional computing systems, such as those based on the Von Neumann architecture, are limited by the physically separated memory and logic units, leading to performance limitations and increased energy consumption [2]. The traditional computing paradigm also struggles with the increasing volume of data, revealing constraints inherent in architectures like the Von Neumann model [3].

Moreover, traditional computing models, particularly in the realm of high-performance and parallel computing, are hindered by control-centric approaches that result in various limitations [4]. The limitations of traditional computing extend to areas like machine learning, where the resilience, versatility, and efficiency of the human brain cannot be effectively mimicked using traditional silicon technology [5]. Furthermore, the limitations of traditional computing are evident in the realm of artificial intelligence, where traditional models face challenges in handling massive parameters efficiently due to the significant computing resources required [6].

Read the full article at Medium


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  2. Li, K. C., et al. (2020). Recent advances in security, privacy, and trust for internet of things (IoT) and cyber-physical systems (CPS). CRC Press.  
  3. Mourelle, L. M. (2022). Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities. N. Nedjah, A. A. Abd El-Latif, & B. B. Gupta (Eds.). Springer. 
  4. Gupta, B. B., & Srinivasagopalan, S. (Eds.). (2020). Handbook of research on intrusion detection systems. IGI Global. 
  5. Raj, B., et al. (Eds.). (2023). AI for big data-based engineering applications from security perspectives. CRC Press. 
  6. Gupta, B. B., & Gupta, D. (Eds.). (2020). Handbook of Research on Multimedia Cyber Security. IGI Global. 

Cite As

Gupta B.B. (2024) Quantum Computing: The Next Frontier in Computational Power, Insights2Techinfo, pp.1

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