a variational toolbox for quantum multi-parameter estimation.

I’m proud to share my first paper as a first-author: In A variational toolbox for quantum multi-parameter estimation, me and my co-authors Johannes Borregaard and Jens Eisert provide a variational quantum algorithm to optimize sensing protocols for quantum multi-parameter estimation.

Quantum technologies are a rapidly expanding field with applications ranging from quantum computers to quantum communication lines. An area with particularly promising application prospects is Quantum Metrology. It exploits quantum effects to enhance the precision of measurements, but making it practical in realistic settings where noise and experimental limitations have to be taken into account is a difficult task. The state of the art revolves around the use of analytic methods and classical simulations, both of which become intractable for larger systems and more complicated noise models and postprocessing.

In our work, we leverage variational methods developed for emerging quantum computers to develop a hybrid variational quantum algorithm that uses a classical feedback loop to learn improved quantum sensing protocols. The algorithm can be run both on the sensing platform itself and on a quantum computer, thereby enabling quantum-aided design of quantum technology on a multitude of experimental platforms, including atomic, photonic and solid-state systems.

We also provide numerical experiments showing that our method both reproduces known results as well as evidence that it allows the study of state of the art experiments in nanoscale NMR. A sample implementation of the algorithm is available in the accompanying PennyLane demonstration.

We hope that our research can aid the development of quantum technologies and to contribute to a shift in mindset that sees quantum technologies themselves as crucial tools to improve future generations of quantum technologies.