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.

翻译：我们提出分层式量子架构搜索（layered-QAS），该策略受经典网络形态学启发，通过渐进式生长与自适应调整来设计参数化量子电路（PQC）架构。参数化量子电路以较少的参数表现出强大的表达能力，但缺乏能为特定学习任务编码归纳偏置的标准架构层（如卷积层、注意力层）。为评估该方法的有效性，我们选择具有挑战性且高度结构化的三维点云分类问题作为应用场景。不同于以往仅将PQC作为经典分类器特征提取器的研究，我们的方法将PQC作为分类模型的核心构建模块。仿真实验表明，分层-QAS能有效缓解贫瘠高原现象，性能优于量子自适应局部搜索和进化搜索基线方法，并在ModelNet数据集上达到基于PQC方法的最优结果。