Quantum Annealing (QA) is an emerging technique, derived from Simulated Annealing, providing metaheuristics for multivariable optimisation problems. Studies have shown that it can be applied to solve NP-hard problems with faster convergence and better quality of result than other traditional heuristics, with potential applications in a variety of fields, from transport logistics to circuit synthesis and optimisation. In this paper, we present a hardware architecture implementing a QA-based solver for the Multidimensional Knapsack Problem, designed to improve the performance of the algorithm by exploiting parallelised computation. We synthesised the architecture using as a target an Altera FPGA board and simulated the execution for solving a set of benchmarks available in the literature. Simulation results show that the proposed implementation is about 100 times faster than a single-thread general-purpose CPU without impact on the accuracy of the solution.

A Parallel Hardware Architecture For Quantum Annealing Algorithm Acceleration / Forno, Evelina; Acquaviva, Andrea; Yuki, Kobayashi; Macii, Enrico; Urgese, Gianvito. - ELETTRONICO. - (2018). (Intervento presentato al convegno IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC 2018) tenutosi a Verona nel 8-10 October 2018) [10.1109/VLSI-SoC.2018.8644777].

A Parallel Hardware Architecture For Quantum Annealing Algorithm Acceleration

FORNO, EVELINA;Andrea Acquaviva;Enrico Macii;Gianvito Urgese
2018

Abstract

Quantum Annealing (QA) is an emerging technique, derived from Simulated Annealing, providing metaheuristics for multivariable optimisation problems. Studies have shown that it can be applied to solve NP-hard problems with faster convergence and better quality of result than other traditional heuristics, with potential applications in a variety of fields, from transport logistics to circuit synthesis and optimisation. In this paper, we present a hardware architecture implementing a QA-based solver for the Multidimensional Knapsack Problem, designed to improve the performance of the algorithm by exploiting parallelised computation. We synthesised the architecture using as a target an Altera FPGA board and simulated the execution for solving a set of benchmarks available in the literature. Simulation results show that the proposed implementation is about 100 times faster than a single-thread general-purpose CPU without impact on the accuracy of the solution.
2018
978-1-5386-4756-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2725886