Automated control of quantum devices


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 isa great tool to achieve this much needed automation. In collaboration with Thomas Ihn lab we made an algorithm that automatically tunes quantum dots to desired states (Phys. Rev. Applied 13, 054019 (2020)) and another one that searches for best experimental samples of graphene, graphite and hBN (Phys. Rev. Applied 13, 064017 (2020) – short live demo here).


Machine learning for condensed matter and quantum information


I am interested how machine learning and condensed matter insights can be useful in quantum information. We combined two challenges important for further scaling of quantum devices: error correction and device verification. We introduced a new scalable approach to quantum error correction implemented on Hamiltonian level (Phys. Rev. Research 1, 033092 (2019)). We also created unsupervised algorithm that helps to determine presence of topological order from experimentally accessible data (New J. Phys. 22 045003 (2020)).


Parameter estimation

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In experiments with quantum devices we do not in most cases have access to measurements for reconstruction of the whole wavefunction. Using probability theory, machine learning or combination of both we can still reconstruct the key parameters for the system dynamics. We implemented these ideas for quantum dots: Phys. Rev. A 96, 052104 (2017) and arXiv:1711.05238 as well as for superconducting qubits: Phys. Rev A 94, 042334 (2016). Currently I am interested in how to expand these methods to scales that cannot be simulated on classical computers.