2023-07-26 –, 32-G449 (Kiva)
The QuantumControl.jl package provides a framework for open-loop quantum optimal control: finding classical control fields to drive the dynamics of a quantum system in a particular way, e.g., to create a particular entangled state or to realize a quantum gate.
Quantum control seeks to design classical "control fields" in order to steer a quantum system in some desired way. It is a cornerstone of modern quantum technology: quantum control is how one generates entangled states for quantum sensing, or realizes the logic gates in a quantum computer. Solving the control problem requires numerically simulating the dynamics of the quantum system and then iteratively tuning the control fields to minimize some figure of merit. I will give an overview of the QuantumControl.jl framework, which implements state of the art methods for simulation and optimization.
Julia provides unique advantages in flexibility and numerical efficiency, both of which are critical to quantum control. Multiple dispatch makes it easy to adapt to different physical systems and efficient representations. Furthermore, integration with the wider Julia ecosystem allows to leverage "semi-automatic differentiation" to efficiently optimize arbitrary, non-analytical figures of merit such as entanglement measures. Benchmarks show that Julia matches the performance of existing Fortran code for quantum control with ease. Using more specialized data structures, such as SparseArrays, StaticArrays or GPUArrays, depending on the use case, can further improve performance by a considerable margin.
I am a senior postdoctoral fellow at the U.S. Army Research Lab in Adelphi, MD.
My research broadly addresses the tools, methods, and applications of quantum technology from a theoretical and numerical perspective. Currently, I am working on applications of machine learning to quantum control, and on the design of robust quantum sensing devices.