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4OR

Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models

Fred Glover, Gary A. Kochenberger, Yu Du

Published Nov 26, 2019• 298 citations
Publisher
DOI
10.1007/s10288-019-00424-y

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Quantum Bridge Analytics relates to methods and systems for hybrid classical-quantum computing and 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 second of a two-part tutorial that surveys key elements of Quantum Bridge Analytics and its applications. Part I focused on the Quadratic Unconstrained Binary Optimization (QUBO) 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. Part II (the present paper) examines an application that augments the use of QUBO models, by disclosing a context for coordinating QUBO solutions through a model we call the Asset Exchange Problem (AEP). Solutions to the AEP enable individuals or institutions to take fuller advantage of solutions to their QUBO models by exchanges of assets that benefit all participants. Such exchanges are generated by an approach called combinatorial chaining, which provides a flexibility to solve AEP variants that open the door to additional links to quantum computing applications. Examples are presented that show the nature of these processes from a tutorial perspective.

•Nov 8, 2019•0 citations
Quantum Bridge Analytics relates to methods and systems for hybrid classical-quantum computing and 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 second of a two-part tutorial that surveys key elements of Quantum Bridge Analytics and its applications. Part I focused on the Quadratic Unconstrained Binary Optimization (QUBO) 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. Part II (the present paper) examines an application that augments the use of QUBO models, by disclosing a context for coordinating QUBO solutions through a model we call the Asset Exchange Problem (AEP). Solutions to the AEP enable individuals or institutions to take fuller advantage of solutions to their QUBO models by exchanges of assets that benefit all participants. Such exchanges are generated by an approach called combinatorial chaining, which provides a flexibility to solve AEP variants that open the door to additional links to quantum computing applications. Examples are presented that show the nature of these processes from a tutorial perspective.