Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers
Paper recorded by Signals 4 on 2026-10-08 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-08 on arXiv · recorded by Signals 4 on 2026-10-09
Category: cs.LG · 机器学习 · first seen 2026-10-09
Abstract
Building a quantum model involves a tradeoff: how complex the circuit should be, and how much training data it needs. Caro et al. show that models with fewer trainable gates need less training data to generalize well. Q-FLAIR shows that a quantum feature-map circuit can be grown gate-by-gate, stopping once further growth stops improving the training loss. We ask whether these two results combine i
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Cite this page: Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers: the #322 most recent of 749 cs.LG papers we have recorded (as of 2026-10-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/toward-joint-optimization-of-circuit-depth-and-training-data-size-in-adaptively-.html
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