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Column generation algorithms for exact modularity maximization in networks. European Journal of Operational Research, 186(2), 504–512.Īloise, D., Cafieri, S., Caporossi, G., Hansen, P., Perron, S., & Liberti, L. A new approach for modeling and solving set packing problems. Journal of Applied Mathematics and Decision Sciences, 9(2), 135–145.Īlidaee, B., Kochenberger, G., Lewis, K., Lewis, M., & Wang, H. A new modeling and solution approach for the number partitioning problem.

Reexamination of the evidence for entanglement in the D-Wave processor. (2015) Decoherence in adiabatic quantum computation. Non-neural network applications for spiking neuromorphic hardware. E., Mniszewsk, S., Reeder, L., Schuman, C. Journal of the ACM (JACM), 55(5), 2Īimone,J. A., Newman (2008) “Aggregating inconsistent information: ranking and clustering.

This document extends an original version published in 4OR to include a section on advanced models related to quantum optimization and a section reporting comparative computational results on challenging combinatorial applications.Īilon, N., Charikar,M. 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. 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. 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.