Elaris Computing Nexus
Quantum Enhanced High Performance Computing for Next Generation AI and Machine Learning
Elaris Computing Nexus
Received On : 12 May 2025
Revised On : 28 June 2025
Accepted On : 24 July 2025
Published On : 02 August 2025
Volume 01, 2025
Pages : 095-107
Abstract
Use of AI/ML is associated with more and more computing power, which often necessitates the application to guzzle energy and take time to train. The paper presents a Quantum-Enhanced High-Performance Computing (Q-HPC) system that will combine traditional HPC units with quantum-assisted optimization to enhance the model training in addition to the predictive accuracy and energy efficiency. The framework that could be used to work with such large volumes of data is multi- GPU/CPU parallelization, and the optimization of parameters and hyperparameters can be implemented with the help of quantum-inspired algorithms. This will lead to a hybrid computation balancing dynamically the classical and quantum computations. Q-HPC was experimented with multiple AI/ML model types, such as convolutional networks, transformer models, graph neural networks and reinforcement learning agents, in which cases it was observed to traverse to the solution faster, more accurately and used less energy than the traditional HPC. It can also be said that the framework is dynamically adaptable and sustainable, i.e. it could be deployed to a massive diversifying range of tasks of AI/ML. The suggested model is a combination of the performance and scalability of the classical HPC with the optimization performance of quantum computing in order to create a new and valuable approach to the next generation AI. It deals with the issues of performance and environmental concerns of high-performance computing.
Keywords
Quantum-Enhanced Computing, Quantum Optimization, Energy Efficiency, Hybrid Computing, Model Acceleration, Next-Generation AI.
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Anandakumar Haldorai, “Quantum Enhanced High Performance Computing for Next Generation AI and Machine Learning”, Elaris Computing Nexus, pp. 095-107, 2025, doi: 10.65148/ECN/2025010.
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© 2025 Anandakumar Haldorai. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.