Journal
NEW JOURNAL OF PHYSICS
Volume 19, Issue -, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.1088/1367-2630/aa8fe6
Keywords
tomograghy; Bayesian; quantum; estimation; learning
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Funding
- US Army Research Office [W911NF-14-1-0098, W911NF14-1-0103]
- Australian Research Council Centre of Excellence for Engineered Quantum Systems
- Australian Research Council [FT130101744]
- Australian Research Council [FT130101744] Funding Source: Australian Research Council
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We introduce a fast and accurate heuristic for adaptive tomography that addresses many of the limitations of prior methods. Previous approaches were either too computationally intensive or tailored to handle special cases such as single qubits or pure states. By contrast, our approach combines the efficiency of online optimization with generally applicable and well-motivated data-processing techniques. Wnumerically demonstrate these advantages in several scenarios including mixed states, higher-dimensional systems, and restricted measurements.
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