Application a quantum machine learning algorithm to the applied problem of modeling the thermal regime of a servers

Abstract

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.

References in corpus (11)

• Quantum Machine Learning
• Quantum machine learning in feature Hilbert spaces
• Quantum Circuit Learning
• Circuit-centric quantum classifiers
• Quantum machine learning beyond kernel methods
• Experimental quantum adversarial learning with programmable superconducting qubits
• A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
• One qubit as a Universal Approximant
• Quantum Kernel Methods under Scrutiny: A Benchmarking Study
• Single-qubit universal classifier implemented on an ion-trap quantum device
• Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator