Classification of Potentially Hazardous Asteroids Using Supervised Quantum Machine Learning
Published in IEEE Access, 2023
This paper, published in IEEE Access, presents a novel approach utilizing supervised quantum machine learning to classify potentially hazardous asteroids (PHAs). It explores the use of quantum computing and artificial intelligence to address challenges in space science, specifically in mitigating the risks posed by asteroids. The study demonstrates enhanced performance over classical machine learning algorithms, achieving significant advancements in computational efficiency and accuracy.
Recommended citation: Bhavsar, R., Jadav, N.K., Bodkhe, U., Gupta, R., Tanwar, S., Sharma, G., Bokoro, P.N., & Sharma, R. (2023). "Classification of Potentially Hazardous Asteroids Using Supervised Quantum Machine Learning." IEEE Access.
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