Verifiable Sampling for Delegated Generative Quantum Machine Learning
Generative machine learning has shown remarkable results across a wide range of interdisciplinary tasks, motivating significant efforts toward Quantum Generative Models as a path to beyond-classical performance, given their capability of generating classically intractable distributions. However, comparatively little attention has been paid to securely delegating such computations to a powerful remote quantum server. In this work, we propose a blind and verifiable protocol for sampling that can be used for the inference phase of such quantum models. Such a protocol inherits blindness from Universal Blind Quantum Computing and achieves verifiability by exploiting known approaches based on t...
| Authors | Matteo Inajetovic, Anna Pappa, Petros Wallden |
|---|---|
| Primary category | quant-ph |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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