无需训练，用进化算法快速搜索高效量子特征映射。
QuProFS: An Evolutionary Training-free Approach to Efficient Quantum Feature Map Search
- 基于电路启发式方法，不依赖训练直接评估量子电路性能。
- 在模拟器和真实量子硬件上均实现高准确率，搜索速度提升2倍。
- 适合希望避开训练瓶颈的量子机器学习研究者使用。
寻找有效的量子特征映射用于数据编码面临巨大挑战，尤其因参数化量子电路存在平坦的训练景观和漫长的训练过程。为此，我们提出一种无需训练的量子架构搜索（QAS）框架，采用聚焦可训练性、硬件鲁棒性、泛化能力、表达力、复杂度及核目标对齐的电路启发式方法。通过多种代理指标对电路架构进行排序，降低评估成本，并引入硬件感知电路以增强抗噪能力。我们在分类任务（使用量子支持向量机）中，针对人工和量子生成的数据集进行了评估。结果表明，该方法在模拟器和真实量子硬件上均表现出色，相较于现有先进QAS方法在采样效率上更具优势，且架构搜索运行时间最高提速2倍。
原文摘要 · Abstract (English)
The quest for effective quantum feature maps for data encoding presents significant challenges, particularly due to the flat training landscapes and lengthy training processes associated with parameterised quantum circuits. To address these issues, we propose an evolutionary training-free quantum architecture search (QAS) framework that employs circuit-based heuristics focused on trainability, hardware robustness, generalisation ability, expressivity, complexity, and kernel-target alignment. By ranking circuit architectures with various proxies, we reduce evaluation costs and incorporate hardware-aware circuits to enhance robustness against noise. We evaluate our approach on classification tasks (using quantum support vector machine) across diverse datasets using both artificial and quantum-generated datasets. Our approach demonstrates competitive accuracy on both simulators and real quantum hardware, surpassing state-of-the-art QAS methods in terms of sampling efficiency and achieving up to a 2x speedup in architecture search runtime.
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