This INSPIRE project is jointly funded by the Quantum Information Science (QIS) Program in the Physics Division in the Mathematical and Physical Sciences Directorate, the Atomic Molecular and Optical Physics Experiment (AMO-E) Program in the Physics Division in the Mathematical and Physical Sciences Directorate, the Algorithmic Foundations (AF) Program in the Computing and Communications Foundations Division in the Computer and Information Science and Engineering Directorate, the Electronics, Photonics and Magnetic Devices (EPMD) Program in the Electrical, Communications and Cyber Systems (ECCS) Division in the Engineering Directorate, and the NSF Office of Integrative Activities (OIA). A revolutionary approach to computing is under development using quantum information processing. However, there is still a gap between the testbeds for quantum information processing that exist today, and the more perfect hardware needed for an ideal quantum computer. This project seeks to fill this gap by finding ways to use variable amounts of quantum behavior to incrementally improve computing systems. The project will examine how to use quantum effects in order to provide benefits in terms of speed, energy, and hardware efficiency for applications in signal processing and machine learning. The project will produce open source software for modeling quantum networks and will train graduate students to develop quantum information processing systems.
This effort builds on work designing circuits for autonomous continuous-time quantum error correction and ultra-low power information processing systems in photonic architectures. The theoretical component of the project will provide tools to study quantum feedback in various computer architectures operating with open quantum systems. This will be done using quantum stochastic differential equations (a form of quantum field theory that is adapted to resemble ordinary stochastic differential equations that are widely used in modern engineering) in order to improve the publicly-available open source software package (Mabuchilab/QNET on GitHub) and explore ways to extend these methods to nano-optomechanics and superconducting circuit QED. The experimental component of this project will complement the high-level study by testing lower-level architectural principles based on coherent-feedback quantum control, going beyond previous work by incorporating nonlinear controller dynamics and pulsed signal fields. Together, the theoretical and experimental parts of this project will advance the quantum information community's ability to rapidly construct rigorous quantum-optical models for complex coherent networks/circuits, facilitating the exploration of new high-level architectural principles.
