A central challenge in quantum machine learning is the design and training of
parameterized quantum circuits (PQCs). Similar to deep learning, vanishing
gradients pose immense problems in the trainability of PQCs, which have been
shown to arise from a multitude of sources. One such cause are non-local loss
functions, that demand the measurement of a large subset of involved qubits. To
facilitate the parameter training for quantum applications using global loss
functions, we propose a Sequential Hamiltonian Assembly, which iteratively
approximates the loss function using local components. Aiming for a prove of
principle, we evaluate our approach using Graph Coloring problem with a
Varational Quantum Eigensolver (VQE). Simulation results show, that our
approach outperforms conventional parameter training by 29.99% and the
empirical state of the art, Layerwise Learning, by 5.12% in the mean accuracy.
This paves the way towards locality-aware learning techniques, allowing to
evade vanishing gradients for a large class of practically relevant problems.

在量子机器学习中，设计和训练参数化量子电路（PQCs）是一个主要挑战。本论文提出了一种顺序哈密顿组装方法，用于处理全局损失函数的参数训练，并通过实验结果证明了该方法在图着色问题中的有效性，表现优于传统的参数训练和层次学习方法，为解决消失梯度问题提供了一种局部感知的学习技术。