Researchers at Multiverse Computing, which delivers quantum computing solutions, and Ikerlan, a technology transfer centre in Spain, 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.
The team found that both algorithms outperformed neural network approaches for classifying manufacturing defects.
Victor Onofre, quantum software developer for Multiverse Computing and one of the authors of the research paper, told Imaging and Machine Vision Europe that the team's approach offers a training time of only six minutes with inference times of two seconds on average. The CNN comparison approach, on the other hand, has training times of hours with many parameters involved.
Read the full article at IMV Europe.
