This research demonstrates quantum machine learning predicts GPU temperatures in high-performance computing, indicating its potential superiority over classical methods.
Quantum computing is a rapidly developed field of science. In the NISC era, quantum machine learning stands out, potentially outperforming classical machine learning. In this paper, we explore the applicability of quantum machine learning to a real-world application. We solved the problem of prediction of the temperature of GPUs in a high-performance computing cluster. We trained a quantum circuit to predict the temperature of each of the eight GPU of each cluster node based on 16 physical parameters. We demonstrated that quantum machine learning performs on this task just as well as classical machine learning. Furthermore, using model problems, we demonstrated that quantum machine learning can potentially outperform classical machine learning.
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Buzin et al. (2025) studied this question.
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