arXiv Machine Learning August 17, 2026 By Marco Roth

Classical Limits of Spectral Filtering in Quantum Generative Models

Read the original on arXiv Machine Learning →

arXiv:2608. 14169v1 Announce Type: cross Abstract: Spectral filtering has been proposed as a route to regularization in quantum generative models: the quantum Fourier transform exposes the amplitude spectrum of a quantum circuit Born machine, and a diagonal filter suppresses the high frequencies associated with finite-sample noise, an operation whose classical counterpart seemingly requires manipulating an exponentially long amplitude vector.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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