让量子神经网络的测量方式自适应变化，提升模型能力。
Adaptive Non-local Observable on Quantum Neural Networks
- 引入随参数演化的非局域可观测量，动态调整测量方式。
- 在分类任务中表现优于传统方法，增强量子比特间关联与信息混合。
- 适合研究量子机器学习与高效量子电路设计的学者。
传统的变分量子线路（VQC）通常依赖于由泡利算符构建的固定厄米可观测量。受海森堡表象启发，我们提出一种自适应的非局域测量框架，显著提升量子线路的模型复杂度。通过引入随演化参数变化的动态厄米可观测量，我们发现优化VQC旋转相当于在可观测量空间中追踪轨迹。这一视角表明，标准VQC仅为海森堡表示的一个特例。此外，将变分旋转与非局域可观测量合理结合，可增强量子比特间的相互作用与信息混合，实现灵活的电路设计。本文提出了两种非局域测量方案，数值模拟在分类任务中验证了该方法优于传统VQC，展现出更强大且资源高效的量子神经网络潜力。
原文摘要 · Abstract (English)
Conventional Variational Quantum Circuits (VQCs) for Quantum Machine Learning typically rely on a fixed Hermitian observable, often built from Pauli operators. Inspired by the Heisenberg picture, we propose an adaptive non-local measurement framework that substantially increases the model complexity of the quantum circuits. Our introduction of dynamical Hermitian observables with evolving parameters shows that optimizing VQC rotations corresponds to tracing a trajectory in the observable space. This viewpoint reveals that standard VQCs are merely a special case of the Heisenberg representation. Furthermore, we show that properly incorporating variational rotations with non-local observables enhances qubit interaction and information mixture, admitting flexible circuit designs. Two non-local measurement schemes are introduced, and numerical simulations on classification tasks confirm that our approach outperforms conventional VQCs, yielding a more powerful and resource-efficient approach as a Quantum Neural Network.
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