Hybrid Quantum and Classic Computing for Future NetworkingSpeaker: Zhu Han – Houston, TX, United States
Topic(s): Applied Computing
AbstractBenefited from the technology development of controlling quantum particles and constructing quantum hardware, quantum computation has attracted more attention in recent year. In communication networks, there are many optimization problems too complex for classical computers, so I attempt to use quantum computation to solve optimization problems. First, we employ quantum computing to solve an optimization problem for network function virtualization (NFV) is a crucial technology for the 5G network development. We build an integer linear programming model of the NFV scheduling problem with the objective of minimizing delays, and transfer it into the quadratic unconstrained binary optimization (QUBO) model. Second, we propose a hybrid quantum Benders’ decomposition algorithm for joint quantum and classic computing. We transfer the Benders’ decomposition’s master problem into the quadratic unconstrained binary optimization (QUBO) model and solve it by the state-of-the-art quantum annealer. Third, we target on feature selection in classification, which is a crucial method to reduce the variables input to a classifier. A QUBO model is introduced to select these features, on a D-wave 2000 qubits quantum annealer.
About this LectureNumber of Slides: 50
Duration: 60 minutes
Languages Available: Chinese (Simplified), English
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