The research team developed a quantum-enhanced kernel method for classification on universal gate-based quantum computers as well as a quantum classification algorithm on a quantum annealer and they found that both algorithms outperformed common classical methods in the identification of relevant images and the accurate classification of manufacturing defects.
“To the best of our knowledge, this research represents the first implementation of quantum computer vision for a relevant problem in a manufacturing production line,” said Ion Etxeberria, CEO of IKERLAN. “This collaborative study confirmed the benefits of applying quantum methods to real-world industrial challenges. We strongly believe that quantum computing will play a key role in providing AI-based solutions to particularly complex scenarios.”
“Quantum machine learning will significantly disrupt the automotive and manufacturing industries,” said Roman Orus, Chief Scientific Officer at Multiverse Computing.
“We are pleased to witness the value of early applications quantum computing today, such as quantum artificial vision, and excited to enter a new era of machine learning alongside forward-thinking partners like IKERLAN as quantum technology continues to advance.”
The co-authored paper, titled “Quantum artificial vision for defect detection in manufacturing,” shows examples of the images analysed by the quantum algorithms and further details the context, metrics and methods used by the researchers and can be downloaded here.
