Quantum Machine Learning thesis explores industrial applications

A new thesis explores Quantum Machine Learning (QML) for industrial applications, addressing challenges in trainability, expressivity, and classical simulation resistance. It introduces subspace-preserving QML algorithms, including photonic circuits and quantum convolutional neural networks, designed to offer polynomial quantum advantage. The research also analyzes variational quantum circuits as quantum Fourier models, establishing conditions for quantum models to provably separate from classical counterparts. AI

RANK_REASON The cluster contains an academic paper published on arXiv detailing theoretical research in Quantum Machine Learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Hamming-weight preserving variational quantum circuits
• Photonic Circuits with Time Delays and Quantum Feedback
• Quantum Convolutional Neural Networks
• quantum Fourier models
• Quantum Machine Learning
• Subspace-preserving QML algorithms
• Variational Quantum Circuits

• paper
• other

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