Quantum Machine Learning Techniques Integrated in Quantum Software Testing

Abstract

Quantum computing (QC) is the era of the mechanisms that quantum solves complex problems faster than classical computers (CCs). Today, there are more opportunities for the public and private sectors to use high-value quantum solutions. QC relies on quantum phenomena to calculate incredible speeds and set for significant expansion. Quantum computers, QC-based Internet of Things (IoT), and communication devices to create, process, and transmit quantum states and entanglement are anticipated to enhance society’s quantum software. In this context, this paper introduces the Quantum Software Testing (QST) approach with Machine Learning (ML) techniques integrated into the Quantum Software Testing Life Cycle (QSTLC). The proposed Quantum Machine Learning Testing (QMLT) exploits ML techniques at critical stages of the QST to enhance testing efficiency, accuracy, and adaptability. Finally, the proposed automated test report generation, summarizing, and knowledge extraction technique facilitates comprehensive documentation and knowledge transfer.