Embedded Files
How do we make quantum technologies better?
How do we make quantum technologies better?
Automation & Control
Experimental realization of new generation of quantum technologies requires precise control of many experimental parameters. In practice these are often adjusted manually, which is not compatible with the scalability goals we have in mind when designing quantum devices. I believe machine learning is a great tool to achieve this much needed automation.
arXiv:2503.12256 (2025)
Nature Nanotechnology (2025)
Phys. Rev. Applied 20, 044081 (2023)
Phys. Rev. Applied 13, 054019 (2020)
Verification & Validation
Verifying that a quantum computer or a simulator is performing the operation we aimed for without a costly tomography is one of the outstanding challenges for contemporary quantum technologies. At QMAI we are approaching this problem on two fronts: designing custom randomized benchmarking protocols and employing modent machine learning tools to reconstruct a robust effective model for the device.
arXiv:2510.18453 (2025)
SciPost Physics (2024)
Phys. Rev. Applied (2023)
Phys. Rev. A (2022)
Simulation & Algorithms
Simulating any quantum device accurately to support and explain experimental finding is a challenge. At QMAI we develop a range of both machine learning and physics driven simulation tools to approach this challenge. This research is intertwined with development of new algorithms realizable of the contemporary quantum computers.
arXiv:2507.09690 (2025)
Mach. Learning: Sci. and Tech. (2025)
SciPost Phys. Codebases (2025)
Phys. Rev. Research (2022)
Materials & Design
Another practical way to improve contemporary quantum technologies is on the level of materials and chip design. At QMAI we are answering the question on how device design choices can improve overall computing or simulation performance. We adapt concepts from condensed matter physics and topology and tailor them to contemporary quantum technologies.
arXiv:2509.26261 (2025)
Phys. Rev. Research (2024)
Phys. Rev. Research (2023)
Phys. Rev. Applied (2022)
The four topics outlined above represent the QMAI's vision towards better, faster and more robust quantum devices. However, in practice, most of our works combine two, three or all four of these key topics. Our vision is to take hardware into account when we design new algorithms, to understand the physics when we automate a challenging task and to test our algorithms across different platforms and types of devices to develop resilient, innovative ideas that will help move our community forward.
While improving quantum technologies is our central goal, our work has found applications in neuroscience (Cell Reports Physical Science (2025) , Frontiers in Neuroscience (2024)) as well as in high-energy physics (arXiv:2509.12323 (2025))
Page updated
Google Sites
Report abuse