Experimentally exploring compressed sensing quantum tomography
Autor: | John Rarity, Alex McMillan, Ingo Roth, Jens Eisert, Will McCutcheon, Bryn Bell, Carlos Riofrio, A. Steffens, Mark Tame |
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Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: |
0301 basic medicine
Physics and Astronomy (miscellaneous) Computer science Materials Science (miscellaneous) FOS: Physical sciences 01 natural sciences Bristol Quantum Information Institute quantum photonics 03 medical and health sciences QETLabs Quantum state 0103 physical sciences Electrical and Electronic Engineering 010306 general physics compressed sensing Quantum Physics Signal processing business.industry Model selection Quantum tomography Atomic and Molecular Physics and Optics Quantum technology 030104 developmental biology Compressed sensing Tomography Photonics business Quantum Physics (quant-ph) Algorithm quantum tomography |
Zdroj: | Steffens, A, Riofrío, C A, Mccutcheon, W, Roth, I, Bell, B A, Mcmillan, A, Tame, M S, Rarity, J G & Eisert, J 2017, ' Experimentally exploring compressed sensing quantum tomography ', Quantum Science and Technology, vol. 2, no. 2, 025005 . https://doi.org/10.1088/2058-9565/aa6ae2 |
DOI: | 10.1088/2058-9565/aa6ae2 |
Popis: | In the light of the progress in quantum technologies, the task of verifying the correct functioning of processes and obtaining accurate tomographic information about quantum states becomes increasingly important. Compressed sensing, a machinery derived from the theory of signal processing, has emerged as a feasible tool to perform robust and significantly more resource-economical quantum state tomography for intermediate-sized quantum systems. In this work, we provide a comprehensive analysis of compressed sensing tomography in the regime in which tomographically complete data is available with reliable statistics from experimental observations of a multi-mode photonic architecture. Due to the fact that the data is known with high statistical significance, we are in a position to systematically explore the quality of reconstruction depending on the number of employed measurement settings, randomly selected from the complete set of data, and on different model assumptions. We present and test a complete prescription to perform efficient compressed sensing and are able to reliably use notions of model selection and cross-validation to account for experimental imperfections and finite counting statistics. Thus, we establish compressed sensing as an effective tool for quantum state tomography, specifically suited for photonic systems. Comment: 12 pages, 5 figures |
Databáze: | OpenAIRE |
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