通过分层生长设计量子电路，提升点云分类性能
Layered Quantum Architecture Search for 3D Point Cloud Classification
- 借鉴经典网络形态学，逐步构建并优化量子电路结构
- 在ModelNet上达到现有量子模型最佳准确率，避免梯度消失问题
- 适合想用量子模型做3D点云分类的研究者
我们提出分层量子架构搜索（layered-QAS），受经典网络形态学启发，通过逐步增长与调整来设计参数化量子电路（PQC）架构。PQC具有高表达能力且参数量少，但缺乏卷积、注意力等编码任务先验偏置的标准层。为验证方法有效性，我们以具有挑战性且结构清晰的3D点云分类任务为研究对象。以往工作仅将PQC作为经典分类器的特征提取器，而本方法将PQC作为分类模型的核心组件。仿真结果显示，所提方法有效缓解了梯度消失（barren plateau）问题，在量子适配的局部和进化型QAS基线之上表现更优，并在ModelNet数据集上实现了基于PQC方法的当前最优结果。
原文摘要 · Abstract (English)
We introduce layered Quantum Architecture Search (layered-QAS), a strategy inspired by classical network morphism that designs Parametrised Quantum Circuit (PQC) architectures by progressively growing and adapting them. PQCs offer strong expressiveness with relatively few parameters, yet they lack standard architectural layers (e.g., convolution, attention) that encode inductive biases for a given learning task. To assess the effectiveness of our method, we focus on 3D point cloud classification as a challenging yet highly structured problem. Whereas prior work on this task has used PQCs only as feature extractors for classical classifiers, our approach uses the PQC as the main building block of the classification model. Simulations show that our layered-QAS mitigates barren plateau, outperforms quantum-adapted local and evolutionary QAS baselines, and achieves state-of-the-art results among PQC-based methods on the ModelNet dataset.
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