aiqbench analysis
This study applies quantum annealing to solve the combinatorial optimization problem of assigning mobile phones to base stations, aiming to improve network communication quality. By introducing a variable reduction formulation, researchers significantly lower qubit requirements and demonstrate superior solution accuracy compared to classical simulated annealing as problem scales increase. The work highlights practical applications for hybrid quantum-classical approaches in telecommunications infrastructure rather than general consumer connectivity standards.
Optimization of Connection Patterns between Mobile Phones and Base Stations using Quantum Annealing
arXiv:2412.19434v2 Announce Type: replace Abstract: In current mobile networks, optimizing which base station a mobile phone in a particular area connects to is crucial for ensuring good communication quality for each mobile phone but presents a challenging combinatorial optimization problem. In this study, we optimize the connection patterns to base stations using quantum annealing which is a heuristic optimization algorithm using quantum fluctuations. However, since the number of qubits on a quantum annealer is limited, it is necessary to consider a formulation that efficiently utilizes qubits. By adopting a variable reduction formulation, we significantly reduce the qubit requirements compared to the naive formulation that is typically used when considering pattern-matching problems. Furthermore, experiments using quantum annealing revealed that the accuracy of the approximate solution obtained by the new formulation is superior to that of the conventional formulation. In addition, we demonstrate that the new formulation provides better solutions than the conventional formulation as the problem size increases, even when using simulated annealing, the classical counterpart of quantum annealing.
Abstract:In current mobile networks, optimizing which base station a mobile phone in a particular area connects to is crucial for ensuring good communication quality for each mobile phone but presents a challenging combinatorial optimization problem. In this study, we optimize the connection patterns to base stations using quantum annealing which is a heuristic optimization algorithm using quantum fluctuations. However, since the number of qubits on a quantum annealer is limited, it is necessary to consider a formulation that efficiently utilizes qubits. By adopting a variable reduction formulation, we significantly reduce the qubit requirements compared to the naive formulation that is typically used when considering pattern-matching problems. Furthermore, experiments using quantum annealing revealed that the accuracy of the approximate solution obtained by the new formulation is superior to that of the conventional formulation. In addition, we demonstrate that the new formulation provides better solutions than the conventional formulation as the problem size increases, even when using simulated annealing, the classical counterpart of quantum annealing.
arXiv:2412.19434 [quant-ph]
(or arXiv:2412.19434v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2412.19434 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1038/s41598-025-09230-5 Focus to learn more DOI(s) linking to related resources
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 262 words
Related coverage

NVIDIA GTC Taipei at COMPUTEX: Live Updates on What’s Next in AI
NVIDIA · May 21, 2026

How Total Surveillance Conquered The Last Sanctuary Of Human Privacy
zerohedge.com · Jul 28, 2026

[AINews] not much happened today
Latent Space · Sep 10, 2026

Natera’s intelligent appointment scheduling with Amazon Bedrock AgentCore
AWS · Aug 26, 2026