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Paper 1
Quantum Computing Systems Implementation and Operations: Technical, Ethical, and National Security Perspectives
Professor of Computer Science and Fellow of the Royal Society Fellow of the British Computer Society (Fellowship, Quantum & Information Security Specialists Committees) American International University West Africa College of Management and Information Technology Kannifing, The Gambia, O. E. Ademola
- Year
- 2025
- Journal
- Advances in Multidisciplinary & Scientific Research Journal Publication
- DOI
- 10.22624/aims/bhi/v11n4p3x
- arXiv
- -
Quantum computing represents a paradigm shift in computational science, offering unprecedented capabilities to solve problems beyond the reach of classical systems. Yet, its implementation and operation involve profound challenges, spanning technical, infrastructural, ethical, and national security dimensions. This article provides a comprehensive analysis of quantum computing systems, examining physical platforms, error correction, qubit connectivity, algorithm design, and industry applications. A case study on national security highlights the urgency of preparing for “Q-Day”—the moment when quantum computers can break classical encryption. Ethical analysis explores privacy, equity, governance, and responsibility, emphasising the need for global frameworks to ensure responsible deployment. By synthesising interdisciplinary perspectives, the study proposes a holistic framework for harnessing quantum computing responsibly, equitably, and securely. Keywords: Quantum computing; National security; Ethical frameworks; Implementation; Systems Operations; Error correction; Infrastructure; Governance Journal Reference Format: Ademola, O.E. (2025): Quantum Computing Systems Implementation and Operations: Technical, Ethical, and National Security Perspectives. Journal of Behavioural Informatics, Digital Humanities and Development Res. Vol. 11 No. 4. Pp 37-52. https://www.isteams.net/behavioralinformaticsjournal . dx.doi.org/10.22624/AIMS/BHI/V11N4P3x
Open paperPaper 2
An efficient quantum algorithm for generative machine learning
Xun Gao, Zhengyu Zhang, Luming Duan
- Year
- 2017
- Journal
- arXiv preprint
- DOI
- arXiv:1711.02038
- arXiv
- 1711.02038
A central task in the field of quantum computing is to find applications where quantum computer could provide exponential speedup over any classical computer. Machine learning represents an important field with broad applications where quantum computer may offer significant speedup. Several quantum algorithms for discriminative machine learning have been found based on efficient solving of linear algebraic problems, with potential exponential speedup in runtime under the assumption of effective input from a quantum random access memory. In machine learning, generative models represent another large class which is widely used for both supervised and unsupervised learning. Here, we propose an efficient quantum algorithm for machine learning based on a quantum generative model. We prove that our proposed model is exponentially more powerful to represent probability distributions compared with classical generative models and has exponential speedup in training and inference at least for some instances under a reasonable assumption in computational complexity theory. Our result opens a new direction for quantum machine learning and offers a remarkable example in which a quantum algorithm shows exponential improvement over any classical algorithm in an important application field.
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