Traffic Flow Optimization Using a Quantum Annealer
By Florian Neukart, Gabriele Compostella, Christian Seidel, David Von Dollen, Sheir Yarkoni, Bob Parney. Frontiers in ICT, 2017
456 citations recorded by the source corpus.
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 the 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 applications. 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.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/82a54cbc476bfb18897f43839089a9970014383d
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Citations in the corpus
- Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models (2019, background): “Recent QUBO quantum computing applications, complementing earlier applications on classical computing systems, include those for graph partitioning problems in [47] and [70]; graph clustering (quantum community detection problems) in [48] [50]; traffic-flow optimization in [51]; vehicle routing prob”
- Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer (2018, background): “Such problems arise naturally in many fields of research, including finance [2], chemistry [3, 4], biology [5, 6], logistics and scheduling [7, 8], and machine learning [9–12].”
- Quantum annealing for industry applications: introduction and review (2021, unknown): “While there is no universal way to set these parameters, simple rules-of-thumb may be found either algebraically or empirically [133, 194].”
- Strawberry Fields: A Software Platform for Photonic Quantum Computing (2018, unknown): “Notably, these methodologies can be applied to tackle important practical problems in chemistry [20–23], finance [24], optimization [25, 26], and machine learning [27–34].”