arXiv:2605.24324v1 Announce Type: cross Abstract: Quantum machine learning is often motivated by the idea that quantum systems can expose useful high-dimensional structure that is difficult to access with classical models. We isolate one central component of this claim: the fixed data-encoding map. Amplitude, angle, and basis encoding are evaluated as deterministic feature maps for classical supervised learning under matched output dimensionality and strong classical controls. The benchmark compares these encodings against raw linear models, random Fourier features, polynomial features, PCA, R
The accelerating pace of AI development and the search for more efficient computational methods are driving research into quantum-inspired machine learning techniques.
This research explores a fundamental component of quantum machine learning, potentially offering insights into novel computational advantages or limitations for classical AI systems.
Understanding the efficacy of quantum-inspired feature maps could inform new directions in algorithm design for complex tasks, potentially bridging classical and quantum computing approaches.
- · Machine learning researchers
- · Quantum computing hardware developers
- · AI algorithm developers
- · Traditional feature engineering methods
Improved understanding of the potential advantages of quantum-inspired techniques over classical feature mapping for machine learning tasks.
Development of hybrid classical-quantum algorithms leveraging specific encoding strategies for enhanced performance.
Potential for quantum advantage in specific AI applications by guiding the design of more effective quantum algorithms and hardware.
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Read at arXiv cs.LG