Quantum Embedding Framework of Industrial Data for Quantum Deep Learning

Abstract

Quantum computing is a contemporary engineering discipline that innovatively overcomes computational burdens. This study applies quantum computing techniques to data analyses with input data issues. When a dataset has insufficient attributes and uncertainties, quantum embedding techniques contribute to the dimensional expansion of input vectors and the quantification of uncertainties. The converted qubits are linked to subsequent deep learning modules, and this architecture is used for accurate data analysis. This study proposes a quantum embedding technique and a corresponding quantum neural network (QNN) to better understand these processes. In this QNN architecture, input data are converted into corresponding qubits, which are transformed with quantum phase-operating modules. The quantum features pass through subsequent deep learning layers for more accurate data analyses. To demonstrate the effectiveness of the proposed model, a process model and relevant analyses are presented and compared with existing deep learning methods.