Abstract
Quantum annealing algorithms belong to the class of meta-heuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum processing units (QPUs) produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks. In this paper, we present a real-world application that uses quantum technologies. Specifically, we show how to map certain parts of a real-world traffic flow optimization problem to be suitable for quantum annealing. We show that time-critical optimization tasks, such as continuous redistribution of position data for cars in dense road networks, are suitable candidates for quantum computing. Due to the limited size and connectivity of current-generation D-Wave QPUs, we use a hybrid quantum and classical approach to solve the traffic flow problem.
Highlights
Quantum annealing technologies such as the quantum processing units (QPUs) made by D-Wave Systems are designed to solve complex combinatorial optimization problems (Johnson et al, 2011)
Evaluating the solutions produced by the D-Wave QPU, the focus was on finding good quality solutions within short periods of calculation
The currently presented problem is a simplified version of traffic flow, as it incorporates only a limited set of cars, no communication to infrastructure, no other traffic participants, and no other optimization targets except minimization of road congestion
Summary
Quantum annealing technologies such as the quantum processing units (QPUs) made by D-Wave Systems are designed to solve complex combinatorial optimization problems (Johnson et al, 2011). Previous experiments have shown how these QPUs implement quantum annealing and that the quantum bits (qubits) in the QPU remain coherent and entangled during the annealing process (Lanting et al, 2014). It has been shown how the quantum properties of qubits play a role in the computation of solutions in both sampling and optimization tasks (O’Gorman et al, 2015; Perdomo-Ortiz et al, 2015; Rieffel et al, 2015; Venturelli et al, 2015a,b; Denchev et al, 2016; Los Alamos National Laboratory, 2016; Raymond et al, 2016). The method presented here is a novel approach to mapping this real-world problem onto a quantum computer
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