论文标题
嵌入混合量子古典神经网络中的学习
Embedding Learning in Hybrid Quantum-Classical Neural Networks
论文作者
论文摘要
量子嵌入学习是将量子机学习应用于经典数据的重要步骤。在本文中，我们提出了一个量子嵌入学习范式的量子，该范式学习可用于训练下游量子机器学习任务有用的嵌入。至关重要的是，我们确定了混合神经网络中的电路旁路问题，在该网络中，学到的经典参数不能有效利用希尔伯特空间。我们观察到，与其他方法相比，少数射击学到的嵌入概括以概括为看不见的类，并且与电路搭桥问题相比，遭受了旁路旁路问题的影响较小。
Quantum embedding learning is an important step in the application of quantum machine learning to classical data. In this paper we propose a quantum few-shot embedding learning paradigm, which learns embeddings useful for training downstream quantum machine learning tasks. Crucially, we identify the circuit bypass problem in hybrid neural networks, where learned classical parameters do not utilize the Hilbert space efficiently. We observe that the few-shot learned embeddings generalize to unseen classes and suffer less from the circuit bypass problem compared with other approaches.
