This project addresses a critical challenge in quantum technologies: reliably controlling quantum systems despite uncertainties and imperfections. Current quantum devices remain highly sensitive to calibration errors, noise, and device variations, limiting scalability and practical deployment.
The project builds on Universally Robust Quantum Control (URQC) [1], which designs control protocols that maintain high fidelity across multiple error types simultaneously. Unlike traditional methods targeting specific errors, URQC exploits geometric properties of quantum dynamics to achieve universal robustness.
In this context, this project will tackle problems of both applied and fundamental nature:
- To create practical tools for programming robust control sequences into cloud quantum computers
- To investigate fundamental limits of quantum control by connecting robustness theory with dynamical complexity measures from many-body physics—quantum chaos, unitary designs, and operator scrambling [2]
- To combine URQC methods with reinforcement learning techniques to tackle robust control of open quantum systems with particle loss [3]
The methodology will integrate analytical optimal control methods, mathematical insights from group theory and many-body physics, numerical optimization using gradient-based [4] and machine learning algorithms, and validation through realistic hardware simulations. The project will deliver new theoretical connections between quantum control and quantum complexity, alongside validated control protocols and open-source software tools enhancing near-term quantum device reliability.