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"Train classical, deploy quantum" requires rethinking generalization

Paper recorded by Signals 4 on 2026-08-31 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.LG · 机器学习 · first seen 2026-09-01

Abstract

Generative models have become central across science and industry, from image and text synthesis to the design of molecules and materials. Quantum generative models are considered one of the most promising applications for quantum computers, since a quantum circuit naturally produces samples from the distribution it encodes, and for suitable circuits that distribution is believed to be hard for an

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#172 most recent of 215 cs.LG papers we have recorded · ↑ newer: On the Complexity of the Compatibility Problem for Succinctly Encoded · ↓ older: Stress-Testing Efficient Responsible-AI Evaluation: When Compute Savin
Cite this page: "Train classical, deploy quantum" requires rethinking generalization: the #172 most recent of 215 cs.LG papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/train-classical-deploy-quantum-requires-rethinking-generalization.html
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