Applying new computing paradigms like quantum computing to the field of machine learning has recently gained attention. However, as high-dimensional real-world applications are not yet feasible to be solved using purely quantum hardware, hybrid methods using both classical and quantum machine learning paradigms have been proposed. For instance, transfer learning methods have been shown to be successfully applicable to hybrid image classification tasks. Nevertheless, beneficial circuit architectures still need to be explored. Therefore, tracing the impact of the chosen circuit architecture and parameterization is crucial for the development of beneficially applicable hybrid methods. However, current methods include processes where both parts are trained concurrently, therefore not allowing for a strict separability of classical and quantum impact. Thus, those architectures might produce models that yield a superior prediction accuracy whilst employing the least possible quantum impact. To tackle this issue, we propose Sequential Quantum Enhanced Training (SEQUENT) an improved architecture and training process for the traceable application of quantum computing methods to hybrid machine learning. Furthermore, we provide formal evidence for the disadvantage of current methods and preliminary experimental results as a proof-of-concept for the applicability of SEQUENT.

翻译：近年来，将量子计算等新兴计算范式应用于机器学习领域引起了广泛关注。然而，由于高维现实世界问题尚无法仅通过纯量子硬件解决，人们提出了融合经典与量子机器学习范式的混合方法。例如，迁移学习方法已被证明可成功应用于混合图像分类任务。尽管如此，仍需探索具有增益性的电路架构。因此，追溯所选电路架构与参数化方案的影响对于开发具有实用价值的混合方法至关重要。然而，现有方法中经典部分与量子部分通常并行训练，导致二者影响无法严格分离。此类架构可能产生高预测精度却仅依赖最低限度量子影响的模型。为解决该问题，我们提出序贯量子增强训练（SEQUENT）——一种改进的架构与训练流程，用于实现量子计算方法在混合机器学习中的可溯源应用。此外，我们从形式上论证了现有方法的局限性，并通过初步实验结果验证了SEQUENT的可行性概念证明。