About

Concentrations

Quantum Communication; Quantum Machine Learning; Data Science/Business Analytics; Physics Education Research; Intellectual Property

Interests

My primary research area is quantum communication and computing, a novel field at the intersection of two significant scientific discoveries of the last century: quantum mechanics and computer science. Recently, engineering has become part of quantum information science as researchers translate the theoretical models to build products for use and commercialization.

• Quantum Communication and Quantum optical implementations – I explore the interface between quantum communication theory and quantum optical implementations. Specifically, I translate abstract quantum key distribution protocols, described by qubits, and physical implementations, described by laser pulses. Notably, I benchmark these implementations to appropriately characterize “quantum advantage” and exploit quantum mechanical structures for use in quantum communication.
• Theory of practical quantum key distribution systems – I develop novel or improve existing security proofs of quantum key distribution (QKD) protocols. The goal of QKD is to generate a shared secret key between two distant parties, Alice and Bob, such that the key is perfectly secret from any eavesdropper, Eve. Most QKD protocols we analyze today have a high symmetry in signals and measurements. This symmetry often allows to perform this optimization analytically. Unfortunately, imperfections in experimental or physical implementations often break the symmetry. Therefore, I develop methods to calculate canonically secret key rates for arbitrary QKD protocols. This method is based on convex optimization theory and allows for efficient numerical evaluations.
• Quantum Information Theory and Computation – During this decade, quantum computers are poised to surpass the computational capabilities of classical computers and will have a transformative impact on numerous industry sectors such as finance and pharmaceuticals. Therefore, I seek to understand how quantum information processing can improve and extend methods in machine learning and applications to finance, such as portfolio optimization, the calculation of risk measures and pricing derivatives, drug discovery, and design.
• Data Science and applications in Higher Education and Business Analytics – Higher Education Institutions (HEIs) have a critical mandate to devise strategies that proficiently allocate resources to improve student performance and faculty experience. Some techniques, such as predictive learning analytics or simply learning analytics, are being embraced to systematically extract information from learning data and develop appropriate approaches to optimize learning and learning environments. Therefore, I seek to use data science and machine learning approaches to identify ways of increasing program personalization and flexibility, improving retention, courses, learning design, and other aspects, leading to student success and satisfaction and influencing future cohorts.

Selected Publications

Book Chapter: Mafu, M. (2023). Leveraging Disruptive Technologies and Systems Thinking Approach at Higher Education Institutions. In: Al-Maadeed, M.A.S.A., Bouras, A., Al-Salem, M., Younan, N. (eds) The Sustainable University of the Future: Reimagining Higher Education and Research (pp. 25-42). Cham: Springer International Publishing.

Sekga, C., Mafu, M., & Senekane, M. (2023). High-dimensional quantum key distribution implemented with biphotons. Scientific Reports, 13(1), 1229.

Mafu, M., Sekga, C., & Senekane, M. (2022). Security of Bennett–Brassard 1984 Quantum-Key Distribution Under a Collective-Rotation Noise Channel. Photonics 2022, 9, 941.

Mathaha, T., Mafu, M., Mabikwa, O. V., Ndenda, J., Hillhouse, G., & Mellado, B. (2022). Leveraging artificial intelligence to optimize COVID-19 robust spread and vaccination roll-out strategies in Southern Africa. Frontiers in Artificial Intelligence, 5, 1013010.

Sekga, C., & Mafu, M. (2021). Reference frame independent twin field quantum key distribution with source flaws. Journal of Physics Communications, 5(4), 045008.

Senekane, M., Mafu, M., & Taele, B. M. (2017). Privacy-preserving quantum machine learning using differential privacy. In 2017 IEEE AFRICON (pp. 1432-1435). IEEE.

McCutcheon, W., Pappa, A., Bell, B. A., Mcmillan, A., Chailloux, A., Lawson, T., Mafu, M., Markham, D., Diamanti, E., Kerenidis, I., Rarity, J.G., & Tame, M. S. (2016). Experimental verification of multipartite entanglement in quantum networks. Nature Communications, 7(1), 1-8.

Mafu, M., Garapo, K., & Petruccione, F. (2014). Finite-key-size security of the Phoenix-Barnett-Chefles 2000 quantum-key-distribution protocol. Physical Review A, 90(3), 032308.

Mafu, M., Dudley, A., Goyal, S., Giovannini, D., McLaren, M., Padgett, M. J., Konrad, T., Petruccione, F., Lütkenhaus, N. & Forbes, A. (2013). Higher-dimensional orbital-angular-momentum-based quantum key distribution with mutually unbiased bases. Physical Review A, 88(3), 032305.

The updated list is found on Google Scholar.