New Design tool to Optimize Quantum Optics Circuits in Silicon

A team led by EECS Associate Professor Boubacar Kanté and EECS Professor Eli Yablonovitch has developed a machine-learning based optimization method for nonlinear and quantum optics. Inverse-design has been traditionally applied to linear optical systems, and it often leads to optimized structures that are unintuitive or experimentally unrealistic. In this study, published in Optica, the researchers attempt to tackle these challenges using a new inverse-design method for nonlinear photon generation. According to lead author and graduate student Zhetao Jia, they were able to achieve a compact, robust, and efficient source of entangled photon pairs based on spontaneous four-wave mixing in silicon, the most common material used in the semiconductor industry. This nonlinear quantum-optics approach could potentially be used for large-scale communication and quantum computing applications.