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Quantum Optimization Quantum Machine Learning

Fairness evaluation during the conceptual design of heat grids with quantum annealers

arXiv
Authors: Kelvin Loh

Year

2019

Paper ID

15388

Status

Preprint

Abstract Read

~2 min

Abstract Words

116

Citations

N/A

Abstract

This paper presents a workflow to evaluate the macro scale thermal fairness for producers in a district heating network during the conceptual design phase of such a network. It uses the workflow to evaluate two types of proposed topologies for a future network to be constructed in Europe. The workflow is also novel in the sense that it has been demonstrated in the paper that within the implementation, the load balancing step can be solved readily by a quantum computing system (in particular, a quantum annealer from DWave). The concept of fairness is also addressed in the workflow and the results show that there exist an optimum number of producers for a given district heating topology.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2019 reference point for readers tracking recent quantum research.
  • This paper presents a workflow to evaluate the macro scale thermal fairness for producers in a district heating network during the conceptual design phase of such a network.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:1910.09929 [2] arXiv https://arxiv.org/abs/1910.09929 [3] Publisher https://arxiv.org/abs/1910.09929

Local Citation Graph (Related-Paper Links)

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