arXiv Machine Learning
Hierarchical Fourier Approximation for Variational Quantum Distribution Learning
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Quantum Machine Learning for Industrial Applications
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Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States
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Beyond the Expressivity-Trainability Paradox: A Dynamical Lie Algebra Perspective on Navigating Barren Plateaus in Quantum Machine Learning
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Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization
arXiv:2606. 10179v1 Announce Type: cross Abstract: Quantum Circuit Born Machines (QCBMs) offer a natural approach to generative machine learning by leveraging the Born rule.
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Empirical Characterization of Learning Geometry in Hybrid Quantum Forecasting Models
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Canonical quantization of neurons
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