(Phys.org) A team of researchers at Harvard University recently developed a quantum circuit-based algorithm inspired by convolutional neural networks (CNNs), a popular machine learning technique that has achieved remarkable results in a variety of fields.
“Our work is largely motivated by recent experimental progress to build quantum computers and the development of artificial intelligence based on neural network methods,” Soonwon Choi, one of the researchers who carried out the study, told Phys.org. “In some sense, the idea to combine machine learning techniques and quantum computers/simulators is very natural: In both fields, we are trying to extract meaningful information from a large amount of complex data.” Choi had often wondered whether there might be a more efficient way of analyzing the large amount of complex data obtained using quantum simulators. Artificial neural networks soon caught his attention.
In their future work, Choi and his colleagues will first try to use their findings to develop new quantum computers. In addition, they would like to carry out further research investigating the relationship between CNNs or other neural network based methods and renormalization techniques.
Harvard Researchers Develop Quantum Circuit-Based Algorithm Inspired by Convolutional Neural Networks
