Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over classical approaches. At this stage, the evidence supporting the performance and advantages of quantum machine learning (QML) models relative to classical models is insufficient.To address this gap, this paper presents an empirical study on the performance of QML models and their classical counterparts. We compare seven model pairs spanning supervised learning and reinforcement learning. Our results indicate that the evaluated quantum machine
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Why these links exist
- Linked via arxiv authorChuanming Yu →
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
- Linked via arxiv authorJiaming Liu →
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
- Linked via arxiv authorZihao Ge →
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
- Linked via arxiv authorXiongfei Wu →
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
- Linked via arxiv authorLulu Zhu →
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
- Linked via arxiv authorPengzhan Zhao →
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
- Linked via arxiv authorJianjun Zhao →
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison