I am currently interested in application of novel machine learning tools and condensed matter insights in quantum information. Recently we finished “Hamiltonian Learning for Quantum Error Correction”, where we combine 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. The paper is published open access here and the code is available on GIT. We also made unsupervised algorithm that helps to determine presence of topological order from experimentally accessible data arXiv:1910.10124 (all code here).
I also work with experimental groups at ETH to automatise search for 2D materials samples as well as tuning of quantum devices. Recently, we developed machine learning driven method to automatically collect suitable hBN samples arXiv:1911.00066 (short live demo here). We also made auto-tuner that find specific charge states of double-quantum dots from small amounts of coarse grained measurements (arXiv:1912.02777).
My quantum optics research is mainly about using quantum measurement theory and machine learning techniques to formulate new ways for quantum parameter estimation. We implemented these ideas for quantum dots: arXiv:1708.06680 and arXiv:1711.05238 as well as for superconducting qubits: arXiv:1608.01814.
On quantum information side, we have proven general theorem about which fermionic Gaussian channels are degradable, i.e. their capacity can be calculated really easily in arXiv:1604.01954.