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dc.contributor.authorKim, Nathan
dc.contributor.authorGarcia, Jeremy
dc.contributor.authorHan, David
dc.date.accessioned2021-02-06T23:36:08Z
dc.date.available2021-02-06T23:36:08Z
dc.date.issued2020-12
dc.identifier.issn2470-3958
dc.identifier.urihttps://hdl.handle.net/20.500.12588/249
dc.description.abstractQuantum computing is a new revolutionary computing paradigm, first theorized in 1981. It is based on quantum physics and quantum mechanics, which are fundamentally stochastic in nature with inherent randomness and uncertainty. The power of quantum computing relies on three properties of a quantum bit: superposition, entanglement, and interference. Quantum algorithms are described by the quantum circuits, and they are expected to solve decision problems, functional problems, oracular problems, sampling tasks and optimization problems so much faster than the classical silicon-based computers. They are expected to have a tremendous impact on the current Big Data technology, machine learning and artificial intelligence. Despite the theoretical and physical advancements, there are still several technological barriers for successful applications of quantum computation. In this work, we review the current state of quantum computation and quantum algorithms, and discuss their implications on the practice of Data Science in the near future. There is no doubt that quantum computing will accelerate the process of scientific discoveries and industrial advancements, having a transformative impact on our society.en_US
dc.language.isoen_USen_US
dc.publisherUTSA Office of Undergraduate Researchen_US
dc.relation.ispartofseriesThe UTSA Journal of Undergraduate Research and Scholarly Work;Volume 7
dc.subjectundergraduate student worksen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectdata scienceen_US
dc.subjectmachine learningen_US
dc.subjectquantum algorithmsen_US
dc.subjectquantum computationen_US
dc.subjectquantum informationen_US
dc.titleQuantum Computation, Quantum Algorithms and Implications on Data Scienceen_US
dc.typePosteren_US
dc.description.departmentManagement Science and Statisticsen_US
dc.description.departmentManagement Science and Statisticsen_US


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