Abstract
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.
Abstract (translated)
我们提出分层量子架构搜索（layered-QAS），一种受经典网络形态学启发的策略，通过逐步增长和调整来设计参数化量子电路（PQC）架构。PQC以较少参数实现强表达能力，但缺乏针对特定学习任务编码归纳偏置的标准架构层（如卷积、注意力）。为评估方法有效性，我们聚焦于3D点云分类这一具有挑战性且高度结构化的问题。此前工作在此任务中仅将PQC用作经典分类器的特征提取器，而我们的方法将PQC作为分类模型的核心构建模块。仿真表明，我们的layered-QAS能缓解 barren plateau 问题，优于量子适配的局部与进化式QAS基线，并在ModelNet数据集上达到基于PQC方法的最先进结果。
URL
https://arxiv.org/abs/2603.20024
