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dc.contributor.authorBorujeni, Sima E.
dc.contributor.authorHarikrishnakumar, Ramkumar
dc.contributor.authorNannapaneni, Saideep
dc.identifier.citationS. E. Borujeni, R. Harikrishnakumar and S. Nannapaneni, "Quantum Grover search-based optimization for innovative material discovery," 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 4486-4489en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractAdvances in data analysis, machine learning, and combinatorial optimization have accelerated the discovery of new materials with desired properties; this paradigm is commonly referred to as Materials 4.0. Quantum computing represents a new computing framework that uses the principles of quantum mechanics such as entanglement and superposition. Quantum algorithms such as Grover search has been proven to have quadratic speedup over the classical search methods. This paper considers the application of Grover search-based combinatorial optimization for material discovery. In particular, this paper considers the identification of the Nickel-Titanium (Ni-Ti) based shape memory alloy of the composition, Ti50 Ni{mathrm {{50-x-y}}} Cux Fey that has the minimum thermal hysteresis associated with the austenite-martensite transformations. The paper details the construction of quantum circuits to carry out the optimization analysis, and demonstrates the analysis on a simulation platform using the Python Qiskit package.en_US
dc.relation.ispartofseriesIEEE International Conference on Big Data, Big Data;2019
dc.subjectShape Memoryen_US
dc.titleQuantum Grover search-based optimization for innovative material discoveryen_US
dc.typeConference paperen_US
dc.rights.holder© 2019 IEEEen_US

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