2000 character limit reached
Utilising a Quantum Hybrid Solver for Bi-objective Quadratic Assignment Problems
Published 27 May 2024 in quant-ph and cs.AI | (2405.17676v1)
Abstract: The intersection between quantum computing and optimisation has been an area of interest in recent years. There have been numerous studies exploring the application of quantum and quantum-hybrid solvers to various optimisation problems. This work explores scalarisation methods within the context of solving the bi-objective quadratic assignment problem using a quantum-hybrid solver. We show results that are consistent with previous research on a different Ising machine.
- Multi-objective qubo solver: bi-objective quadratic assignment problem. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’22, page 467–475, New York, NY, USA, 2022a. Association for Computing Machinery. ISBN 9781450392372. doi:10.1145/3512290.3528698. URL https://doi.org/10.1145/3512290.3528698.
- A study of scalarisation techniques for multi-objective qubo solving. In International Conference on Operations Research, pages 393–399. Springer, 2022b.
- Applying ising machines to multi-objective qubos. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, GECCO ’23 Companion, page 2166–2174, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 9798400701207. doi:10.1145/3583133.3596312. URL https://doi.org/10.1145/3583133.3596312.
- An improved dimension-sweep algorithm for the hypervolume indicator. In 2006 IEEE international conference on evolutionary computation, pages 1157–1163. IEEE, 2006.
- J. Blank and K. Deb. pymoo: Multi-objective optimization in python. IEEE Access, 8:89497–89509, 2020.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.