Model Predictive Control for Finite Input Systems using the D-Wave Quantum Annealer

Abstract

The D-Wave quantum annealer has emerged as a novel computational architecture that is attracting significant interest, but there have been only a few practical algorithms exploiting the power of quantum annealers. Here we present a model predictive control (MPC) algorithm using a quantum annealer for a system allowing a finite number of input values. Such an MPC problem is classified as a non-deterministic polynomial-time-hard combinatorial problem, and thus real-time sequential optimization is difficult to obtain with conventional computational systems. We circumvent this difficulty by converting the original MPC problem into a quadratic unconstrained binary optimization problem, which is then solved by the D-Wave quantum annealer. Two practical applications, namely stabilization of a spring-mass-damper system and dynamic audio quantization, are demonstrated. For both, the D-Wave method exhibits better performance than the classical simulated annealing method. Our results suggest new applications of quantum annealers in the direction of dynamic control problems.

11 pages, 4 figures, manuscript accepted in Scientific Reports

References in corpus (9)

• Quantum Annealing in the Transverse Ising Model
• Experimental investigation of performance differences between Coherent Ising Machines and a quantum annealer
• What is the Computational Value of Finite Range Tunneling?
• A practical heuristic for finding graph minors
• Nonnegative/binary matrix factorization with a D-Wave quantum annealer
• Quantum Annealing Implementation of Job-Shop Scheduling
• Bayesian Network Structure Learning Using Quantum Annealing
• Training a Binary Classifier with the Quantum Adiabatic Algorithm
• Training a Large Scale Classifier with the Quantum Adiabatic Algorithm

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• Using quantum computers in control: interval matrix properties