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arXiv Machine Learning By Matteo Inajetovic, Anna Pappa, Petros Wallden

Verifiable Sampling for Delegated Generative Quantum Machine Learning

Read the original on arXiv Machine Learning →

The paper introduces a blind and verifiable protocol for sampling from quantum generative models, enabling secure inference on a remote quantum server. It builds on Universal Blind Quantum Computing for blindness and uses trap-based techniques for verifiability, also estimating the proportion of corrupted samples. Additionally, the authors extend their earlier work on verifiable delegated variational quantum algorithms to cover the training phase of such models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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