Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78657
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dc.contributor.authorPi, Shuheng
dc.date.accessioned2019-06-25T05:53:22Z
dc.date.available2019-06-25T05:53:22Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/78657
dc.description.abstractInformation entropy, the expected amount of information produced by a random data source, has been a topic of interest since Shannon introduced the concept in 1948. The concept was later generalised to Rényi and Tsalli entropies for different research applications in the classical information theory. The information entropy becomes increasingly important when dealing with data compression and cryptography in this data-driven era which requires efficient management of the big data and effective extraction of meaningful information. Hence, in this final year project report, the author introduces the new representation of von Neumann entropy with less computational effort and new representations of Rényi and Tsalli in the quantum information domain. Meanwhile, the author investigates mathematically the feasibility of the proposed representations of von Neumann, Rényi and Tsalli entropies. Subsequently, the new representations are validated through MATLAB and case studies.en_US
dc.format.extent97 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Science::Physics::Atomic physics::Quantum theoryen_US
dc.titleQuantum information entropyen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorShu Jian Junen_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.degreeBachelor of Engineering (Mechanical Engineering)en_US
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Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)
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