TL;DR
This paper investigates the use of a quantum-hybrid solver with scalarisation techniques to address bi-objective quadratic assignment problems, demonstrating results aligned with prior studies on Ising machines.
Contribution
It introduces a quantum-hybrid approach with scalarisation for bi-objective quadratic assignment, expanding quantum optimization applications.
Findings
Results consistent with previous Ising machine research
Demonstrates effectiveness of quantum-hybrid solvers for bi-objective problems
Highlights potential of quantum approaches in complex optimization
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
