Abstract
Quantum Bridge Analytics relates generally to methods and systems for hybrid classical-quantum computing, and more particularly is devoted to developing tools for bridging classical and quantum computing to gain the benefits of their alliance in the present and enable enhanced practical application of quantum computing in the future. This is the first of a two-part tutorial that surveys key elements of Quantum Bridge Analytics and its applications, with an emphasis on supplementing models with numerical illustrations. In Part 1 (the present paper) we focus on the Quadratic Unconstrained Binary Optimization model which is presently the most widely applied optimization model in the quantum computing area, and which unifies a rich variety of combinatorial optimization problems.
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This adds a constant to the QUBO model, which is irrelevant for optimization.
Reference to quantum computing would not be complete without mentioning Google’s recent claim to achieving ‘quantum supremacy.’ This outcome has no bearing on the computational considerations discussed here. See, for example Preskill (2019).
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Acknowledgements
This tutorial was influenced by our collaborations on many papers over recent years with several colleagues to whom we owe a major debt of gratitude. These co-workers, listed in alphabetical order, are: Bahram Alidaee, Dick Barr, Andy Badgett, Rajesh Chawla, Yu Du, Jin-Kao Hao, Mark Lewis, Karen Lewis, Zhipeng Lu, Abraham Punnen, Cesar Rego, Yang Wang, Haibo Wang and Qinghua Wu. Other collaborators whose work has inspired us are too numerous to mention. Their names may be found listed as our coauthors on our home pages.
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Glover, F., Kochenberger, G. & Du, Y. Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models. 4OR-Q J Oper Res 17, 335–371 (2019). https://doi.org/10.1007/s10288-019-00424-y
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DOI: https://doi.org/10.1007/s10288-019-00424-y
Keywords
- Quadratic Unconstrained Binary Optimization (QUBO)
- Quantum computing
- Quantum Bridge Analytics
- Combinatorial optimization