Abstract
Quantum computation uses quantum mechanical principles to reach beyond-classical computational power. This has endless applications, especially in optimisation-problems’ solving. Most of today’s quantum optimisers, more specifically, Quantum Approximate Optimisation Algorithm (QAOA), were originally designed to solve single-objective problems, although real-life scenarios include generally dealing with multiple objectives. Very preliminary literature with design/implementation limitations has been done in this sense. This makes dealing with such limitations and expanding the QAOA applicability to multi-objective optimisation an important step towards advancing quantum computation. To do so, this work presents a decomposition-based Multi-Objective QAOA (MO-QAOA) able to solve multi-objective problems. The proposal’s design explores QAOA’s features considering the error-prone and limited nature of today’s quantum computers as well as the costly quantum simulation. This work’s contributions stand in designing both, (I) sequential and parallel MO-QAOA, based on (II) weighted-sum and Tchebycheff scalarisation, by (III) exploring the QAOA’s parameters’ transference. The validation has been done using 2, 3 and 4-objectives problems of several sizes/complexities/types, using up to 2000 slaves/jobs running quantum computer simulators, as well as three real IBM 127-qubits’ quantum computers. The results show up to 89% execution-time decrease, which supports the applicability/reliability of the proposal in today’s time-constrained and error-prone quantum computers.
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Acknowledgments
This research is partially funded by (I) the PID 2020-116727RB-I00 (HUmove) funded by MCIN/AEI/10.13039/501100011033, (II) TAILOR ICT-48 Network (No 952215) funded by EU Horizon 2020 research and innovation programme, and (III) the Junta de Andalucia (Spain), under contract QUAL21 010UMA. The authors thank the Supercomputing and Bioinnovation Center (SCBI) of the University of Malaga for their provision of computational resources/technical support. Enrique Alba declares that the views expressed are purely those of the writer and may not in any circumstances be regarded as stating an official position of the European Commission.
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Dahi, Z.A., Chicano, F., Luque, G., Derbel, B., Alba, E. (2024). Scalable Quantum Approximate Optimiser for Pseudo-Boolean Multi-objective Optimisation. In: Affenzeller, M., et al. Parallel Problem Solving from Nature – PPSN XVIII. PPSN 2024. Lecture Notes in Computer Science, vol 15151. Springer, Cham. https://doi.org/10.1007/978-3-031-70085-9_17
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