# Preprint cuts encryption depth for shared quantum model training

_Published Tuesday, September 29, 2026 at 9:07 AM EDT · Science, AI · Latest · Tier 2 — Notable_

Researchers report a way to train quantum neural networks across separate clients while keeping their updates encrypted, without the costly encryption resets previously associated with rotation weights. Their preprint represents each rotation as a unit quaternion, making the operation a low-degree calculation that uses one multiplicative level of homomorphic encryption; averaging updates uses none.

The authors tested two cryptographic backends and studied training with up to 20 clients. On a 156-qubit processor, the encrypted approach achieved 0.9918 fidelity against 0.99957 for an unencrypted control. The results remain preprint findings, and the measured hardware fidelity is lower than the control.

## Sources

- [cs.LG updates on arXiv.org](https://arxiv.org/abs/2609.30581)

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Canonical: https://techandbusiness.org/newswire/IlGP6A9LHI5NiWZ2xvEUOg
Published: 2026-09-29T13:07:46.066Z
Story chronology: 2026-09-29T04:00:00.000Z
Retrieved: 2026-09-29T15:02:19.686Z
Publisher: Tech & Business (techandbusiness.org)