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Task-aligned spectral inductive biases in parameterized quantum circuits

Paper recorded by Signals 4 on 2026-10-16 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-16 on arXiv · recorded by Signals 4 on 2026-10-19

Category: cs.LG · 机器学习 · first seen 2026-10-19

Abstract

Parameterized quantum circuits are widely investigated in quantum machine learning, yet how their architecture can provide task-aligned inductive biases remains poorly understood. We study data reuploading quantum circuits with two distinct inputs: a signal that varies between samples and a fixed, task-defined spectral query that specifies how the signal should be probed. We show that encoding the

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#247 most recent of 749 cs.LG papers we have recorded · ↑ newer: HarnessPro: Recursive Harness Self-Improvement with Pareto Optimizatio · ↓ older: Beyond the Answer: Student-Conditioned Trace Construction for Reasonin
Cite this page: Task-aligned spectral inductive biases in parameterized quantum circuits: the #247 most recent of 749 cs.LG papers we have recorded (as of 2026-10-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/task-aligned-spectral-inductive-biases-in-parameterized-quantum-circuits.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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