Seminar: Akram Youssry, UTS QSI

Presenter: Akram Youssry, UTS Centre for Quantum Software and Information

Title: Modeling and Control of a Reconfigurable Photonic Circuit using Deep Learning   

Abstract:
The complexity of experimental quantum information processing devices is increasing rapidly, requiring new approaches to control them. In this work, we address the problems of practically modeling and controlling an integrated optical waveguide array chip—a technology expected to have many applications in telecommunications and optical quantum information processing. This photonic circuit can be electrically reconfigured, but only the output optical signal can be monitored. As a result, the conventional control methods cannot be naively applied. Characterizing such a chip is challenging for a number of reasons. We have developed a deep neural network approach to solve these problems. The architecture is designed specifically to overcome the challenges using a Gated Recurrent Unit (GRU)-based network as the central component. The Hamiltonian is estimated as a blackbox, while the rules of quantum mechanics such as state evolution is embedded in the structure as a whitebox. The resulting overall graybox model of the chip shows good performance both quantitatively in terms of the mean square error and qualitatively in terms of the shape of the predicted waveforms. We use this neural network to solve a classical and a quantum control problem. In the classical application we find a control sequence to approximately realize a time-dependent output power distribution. For the quantum application we obtain the control voltages to realize a target set of quantum gates. The method we propose is generic and can be applied to other systems that can only be probed indirectly.