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:应用量子计算等新计算模式到机器学习领域最近引起了注意,然而,由于使用纯量子硬件尚不可行,无法解决高维现实世界应用的问题,因此提出了使用古典和量子机器学习模式的混合方法,例如,转移学习方法被证明成功地适用于混合图像分类任务,然而,仍然需要探索有益的电路结构,因此,追踪所选择的电路结构和参数化的影响对于开发有益适用的混合方法至关重要。然而,目前的方法包括两个部分同时接受培训的过程,因此不允许对古典和量子影响进行严格的分离。因此,这些结构可能会产生在使用最小量子影响的同时产生更精确预测的模型。为了解决这一问题,我们建议按顺序量子强化培训(SEQUENT)改进结构和培训过程,以追踪量子计算方法应用于混合机器学习的可追踪应用。此外,我们正式证明当前方法和初步实验结果作为SEQUENT适用性证据的不利之处。