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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

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Paper 2

Tackling Sampling Noise in Physical Systems for Machine Learning Applications: Fundamental Limits and Eigentasks

Fangjun Hu, Gerasimos Angelatos, Saeed A. Khan, Marti Vives, Esin Türeci, Leon Bello, Graham E. Rowlands, Guilhem J. Ribeill, Hakan E. Türeci

Year: 2023
Journal: arXiv preprint
DOI: arXiv:2307.16083
arXiv: 2307.16083

The expressive capacity of physical systems employed for learning is limited by the unavoidable presence of noise in their extracted outputs. Though present in physical systems across both the classical and quantum regimes, the precise impact of noise on learning remains poorly understood. Focusing on supervised learning, we present a mathematical framework for evaluating the resolvable expressive capacity (REC) of general physical systems under finite sampling noise, and provide a methodology for extracting its extrema, the eigentasks. Eigentasks are a native set of functions that a given physical system can approximate with minimal error. We show that the REC of a quantum system is limited by the fundamental theory of quantum measurement, and obtain a tight upper bound for the REC of any finitely-sampled physical system. We then provide empirical evidence that extracting low-noise eigentasks can lead to improved performance for machine learning tasks such as classification, displaying robustness to overfitting. We present analyses suggesting that correlations in the measured quantum system enhance learning capacity by reducing noise in eigentasks. The applicability of these results in practice is demonstrated with experiments on superconducting quantum processors. Our findings have broad implications for quantum machine learning and sensing applications.

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