Machine learning techniques have so far proved to be very promising for the analysis of data in several fields, with many potential applications. However, researchers have found that applying these methods to quantum physics problems is far more challenging due to the exponential complexity of many-body systems.
Quantum many-body systems are essentially microscopic structures made up of several interacting particles. While quantum physics studies have focused on the collective behavior of these systems, using machine learning in these investigations has proven to be very difficult.
With this in mind, Grad student Iris Cong, 2018 PhD graduate Soonwon Choi, and Prof. Mikhail Lukin 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. In their paper, published in Nature Physics, the researchers outlined this new architecture and evaluated its accuracy in recognizing quantum states associated with a 1-D, symmetry-protected topological phase.
Continue reading "Introducing quantum convolutional neural networks" by Ingrid Fadelli on Phys.org, Sep 10, 2019. https://phys.org/news/2019-09-quantum-convolutional-neural-networks.html
Also read the original research article: I. Cong, S. Choi & M.D. Lukin, "Quantum convolutional neural networks," Nature Physics (2019) https://doi.org/10.1038/s41567-019-0648-8.