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QUANTUM COMBINATORIAL DESIGN

Published online by Cambridge University Press:  27 July 2021

James Gopsill ,
Guy Johns  and
Ben Hicks
James Gopsill*
Affiliation:
Design Manufacturing Futures Lab, School of Civil, Aerospace and Mechanical Engineering, University of Bristol, UK; Centre for Modelling and Simulation, Bristol, UK
Guy Johns
Affiliation:
Centre for Modelling and Simulation, Bristol, UK
Ben Hicks
Affiliation:
Design Manufacturing Futures Lab, School of Civil, Aerospace and Mechanical Engineering, University of Bristol, UK;
*
Gopsill, James, University of Bristol, Mechanical Engineering, United Kingdom, [email protected]

Abstract

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Combinatorial Design such as configuration design, design optioneering, component selection, and generative design, is common across engineering. Generating solutions for a combinatorial design task often involves the application of classical computing solvers that can either map or navigate design spaces. However, it has been observed that classical computing resource power-law scales with many design space models. This observation suggests classical computing may not be capable of modelling our future design space needs.

To meet future design space modelling needs, this paper examines quantum computing and the characteristics that enables its resources to scale polynomially with design space size. The paper then continues to present a combinatorial design problem that is subsequently represented, constrained and solved by quantum computing. The results of which are the derivation of an initial set of circuits that represent design space constraints. The study shows the game-changing possibilities of quantum computing as an engineering design tool and is the start of an exciting new journey for design research.

Keywords

Constraint modellingRobust designSimulationQuantum ComputingDesign Space
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2511 - 2520
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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