  Quantum computing (QC) and deep learning techniques have attracted widespread
attention in the recent years. This paper proposes QC-based deep learning
methods for fault diagnosis that exploit their unique capabilities to overcome
the computational challenges faced by conventional data-driven approaches
performed on classical computers. Deep belief networks are integrated into the
proposed fault diagnosis model and are used to extract features at different
levels for normal and faulty process operations. The QC-based fault diagnosis
model uses a quantum computing assisted generative training process followed by
discriminative training to address the shortcomings of classical algorithms. To
demonstrate its applicability and efficiency, the proposed fault diagnosis
method is applied to process monitoring of continuous stirred tank reactor
(CSTR) and Tennessee Eastman (TE) process. The proposed QC-based deep learning
approach enjoys superior fault detection and diagnosis performance with
obtained average fault detection rates of 79.2% and 99.39% for CSTR and TE
process, respectively.
