SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training
This addresses the need for traceable quantum computing methods in hybrid machine learning, though it appears incremental as it builds on existing hybrid approaches.
The paper tackles the problem of untraceable quantum impact in hybrid quantum-classical machine learning by proposing SEQUENT, an improved architecture and training process that enables strict separability, with preliminary experimental results showing its applicability as a proof-of-concept.
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.