{"department":[{"_id":"27"},{"_id":"623"},{"_id":"15"}],"project":[{"name":"PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"citation":{"chicago":"Schapeler, Timon, Robert Schade, Michael Lass, Christian Plessl, and Tim Bartley. “Scalable Quantum Detector Tomography by High-Performance Computing.” <i>ArXiv:2404.02844</i>, 2024.","mla":"Schapeler, Timon, et al. “Scalable Quantum Detector Tomography by High-Performance Computing.” <i>ArXiv:2404.02844</i>, 2024.","short":"T. Schapeler, R. Schade, M. Lass, C. Plessl, T. Bartley, ArXiv:2404.02844 (2024).","ama":"Schapeler T, Schade R, Lass M, Plessl C, Bartley T. Scalable quantum detector tomography by high-performance computing. <i>arXiv:240402844</i>. Published online 2024.","ieee":"T. Schapeler, R. Schade, M. Lass, C. Plessl, and T. Bartley, “Scalable quantum detector tomography by high-performance computing,” <i>arXiv:2404.02844</i>. 2024.","apa":"Schapeler, T., Schade, R., Lass, M., Plessl, C., &#38; Bartley, T. (2024). Scalable quantum detector tomography by high-performance computing. In <i>arXiv:2404.02844</i>.","bibtex":"@article{Schapeler_Schade_Lass_Plessl_Bartley_2024, title={Scalable quantum detector tomography by high-performance computing}, journal={arXiv:2404.02844}, author={Schapeler, Timon and Schade, Robert and Lass, Michael and Plessl, Christian and Bartley, Tim}, year={2024} }"},"type":"preprint","year":"2024","author":[{"orcid":"0000-0001-7652-1716","id":"55629","first_name":"Timon","last_name":"Schapeler","full_name":"Schapeler, Timon"},{"full_name":"Schade, Robert","last_name":"Schade","first_name":"Robert","id":"75963","orcid":"0000-0002-6268-5397"},{"id":"24135","first_name":"Michael","last_name":"Lass","full_name":"Lass, Michael","orcid":"0000-0002-5708-7632"},{"orcid":"0000-0001-5728-9982","first_name":"Christian","id":"16153","last_name":"Plessl","full_name":"Plessl, Christian"},{"first_name":"Tim","id":"49683","full_name":"Bartley, Tim","last_name":"Bartley"}],"user_id":"55629","_id":"53202","status":"public","date_created":"2024-04-04T08:43:18Z","external_id":{"arxiv":["2404.02844"]},"publication":"arXiv:2404.02844","abstract":[{"lang":"eng","text":"At large scales, quantum systems may become advantageous over their classical\r\ncounterparts at performing certain tasks. Developing tools to analyse these\r\nsystems at the relevant scales, in a manner consistent with quantum mechanics,\r\nis therefore critical to benchmarking performance and characterising their\r\noperation. While classical computational approaches cannot perform\r\nlike-for-like computations of quantum systems beyond a certain scale, classical\r\nhigh-performance computing (HPC) may nevertheless be useful for precisely these\r\ncharacterisation and certification tasks. By developing open-source customised\r\nalgorithms using high-performance computing, we perform quantum tomography on a\r\nmegascale quantum photonic detector covering a Hilbert space of $10^6$. This\r\nrequires finding $10^8$ elements of the matrix corresponding to the positive\r\noperator valued measure (POVM), the quantum description of the detector, and is\r\nachieved in minutes of computation time. Moreover, by exploiting the structure\r\nof the problem, we achieve highly efficient parallel scaling, paving the way\r\nfor quantum objects up to a system size of $10^{12}$ elements to be\r\nreconstructed using this method. In general, this shows that a consistent\r\nquantum mechanical description of quantum phenomena is applicable at everyday\r\nscales. More concretely, this enables the reconstruction of large-scale quantum\r\nsources, processes and detectors used in computation and sampling tasks, which\r\nmay be necessary to prove their nonclassical character or quantum computational\r\nadvantage."}],"title":"Scalable quantum detector tomography by high-performance computing","date_updated":"2024-04-16T10:09:35Z","language":[{"iso":"eng"}]}