As ever larger and more complex quantum devices are constructed, a key challenge is to control them in a way that preserves their fragile quantum nature. To achieve the required level of control, it is essential to precisely identify crucial properties and features of the quantum system, material, or process of interest.

This project addresses the identification of key properties by using a bootstrapping approach, combining today’s small quantum computers with large-scale classical computing resources to design the next generation of quantum computers. This approach will allow to systematic refining of large quantum systems, and engineering of devices with better functional properties such as intrinsic resistance to errors.

Specifically, we use a combination of machine learning to screen candidate materials with desired properties, quantum simulation of promising systems using available intermediate-scale quantum processors to refine adaptive learning strategies, and experimental validation of the fundamental microscopic material properties. The transformative goal of this research is to develop improved robust and accurate control of large-scale quantum systems.

By integrating big data, quantum simulation, and experimental validation to solve fundamental challenges in quantum information science, we aim to  build a unique center of excellence for quantum information science.