FSU Researchers Report Enhanced Quantum Machine Learning Algorithms - insideHPC

Florida State University researchers report they have found a way to automatically infer parameters used in an important quantum Boltzmann machine algorithm for machine learning applications. The work could help build artificial neural networks used for training computers to solve complicated, interconnected problems, such as image recognition, drug discovery and the creation of new materials. The findings of Professor William Oates, the Cummins Inc. Professor in Mechanical Engineering and chair of the Department of Mechanical Engineering at the FAMU-FSU College of Engineering, and postdoctoral researcher Guanglei Xu were published in Scientific Reports. "There's a belief that quantum computing, as it comes online and grows in computational power, can provide you with some new tools, but figuring out how to program it and how to apply it in certain applications is a big question," said Oates. Quantum bits, unlike binary bits in a standard computer, can exist in more than one state at a time, a concept known as superposition.