I am interested in modelling, simulating, learning and controlling quantum systems with applications in quantum technologies, often related to spin dynamics. A particular interesting aspect of this is to learn physical laws of a system to enable to control it. Applications are focused on quantum spin networks, spintronics, magnetic resonance imaging and spectroscopy, and chemical synthesis.
Irtaza Khalid, Carrie Weidner, S.G. Schirmer, Edmond Jonckheere, Frank C. Langbein. Finding and Characterising Robust Quantum Controls. Poster, BQIT:22, 2022. [PDF:poster]
E. Jonckheere, S. G. Schirmer, F. C. Langbein, C. A. Weidner. Noiseless robust performance with structured uncertainties and initial state error. Preprint 2022. [PDF]
SG Schirmer, FC Langbein, CA Weidner, EA Jonckheere. Robustness of Quantum Systems Subject to Decoherence: Structured Singular Value Analysis?. IEEE Conf Decision and Control, pp. 4158-4163, 2021. [DOI:10.1109/CDC45484.2021.9682796] [arXiv:2110.05372] [PDF]
I. Khalid, C. A. Weidner, E. A. Jonckheere, S. G. Schirmer, F. C. Langbein. Reinforcement Learning vs. Gradient-Based Optimisation for Robust Energy Landscape Control of Spin-1/2 Quantum Networks. IEEE Conf Decision and Control, pp. 4133-4139, 2021. [DOI:10.1109/CDC45484.2021.9683463] [arXiv:2109.07226] [PDF]
Sophie G. Schirmer, Frank C. Langbein, Carrie A. Weidner, Edmond Jonckheere. Robust Control Performance for Open Quantum Systems. IEEE Trans Automatic Control, 67(11):6012-6024, 2022. [DOI:10.1109/TAC.2022.3181249] [arXiv:2008.13691] [PDF]
FC Langbein, SG Shermer, S O’Neil, E Jonckheere. MatSpinNet. Code, https://qyber.black/spinnet/code-matspinnet, https://github.com/qyber-black/Code-MatSpinNet. [DOI:10.6084/m9.figshare.21856911]
E. Jonckheere, S. Schirmer, F. C. Langbein. Effect of Quantum Mechanical Global Phase Factor on Error vs Sensitivity Limitation in Quantum Routing. Accepted, 58th IEEE Conf. Decision and Control (CDC), 2019. [DOI:10.1109/CDC40024.2019.9029913] [PDF]
M. Chandler, C. Jenkins, S. M. Shermer, F. C. Langbein. MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Network. Submitted, 2019. [arxiv:1909.03836] [MRSNet]
C. Jenkins, M. Chandler, F. C. Langbein, S. M. Shermer. Benchmarking GABA Quantification: A Ground Truth Data Set and Comparative Analysis of TARQUIN, LCModel, jMRUI and Gannet. Submitted, 2021. [arxiv:1909.02163] [PDF]
M. Chandler, C. Jenkins, S. M. Shermer, F. C. Langbein. MRSNet, V1.0. Code, https://qyber.black/MRIS/mrsnet, https://github.com/MaxChandler/MRSNet, 16th August 2019. [DOI:10.6084/m9.figshare.9824417.v1]
S.M. Shermer, C. Jenkins, M. Chandler, F.C. Langbein. Magnetic resonance spectroscopy data for GABA quantification using MEGAPRESS pulse sequence. Data set, IEEE Data Port, 15th August 2019. [DOI:10.21227/ak1d-3s20] [ZIP]