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Quantum Machine Learning

Potential Energy Advantage of Quantum Economy

arXiv
Authors: Junyu Liu, Hansheng Jiang, Zuo-Jun Max Shen

Year

2023

Paper ID

55765

Status

Preprint

Abstract Read

~2 min

Abstract Words

157

Citations

N/A

Abstract

Energy cost is increasingly crucial in the modern computing industry with the wide deployment of large-scale machine learning models and language models. For the firms that provide computing services, low energy consumption is important both from the perspective of their own market growth and the government's regulations. In this paper, we study the energy benefits of quantum computing vis-a-vis classical computing. Deviating from the conventional notion of quantum advantage based solely on computational complexity, we redefine advantage in an energy efficiency context. Through a Cournot competition model constrained by energy usage, we demonstrate quantum computing firms can outperform classical counterparts in both profitability and energy efficiency at Nash equilibrium. Therefore quantum computing may represent a more sustainable pathway for the computing industry. Moreover, we discover that the energy benefits of quantum computing economies are contingent on large-scale computation. Based on real physical parameters, we further illustrate the scale of operation necessary for realizing this energy efficiency advantage.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2023 reference point for readers tracking recent quantum research.
  • Energy cost is increasingly crucial in the modern computing industry with the wide deployment of large-scale machine learning models and language models.

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