Enhanced ancilla-assisted calibration of a measuring apparatus
G. Brida, L. Ciavarella, Ivo Pietro Degiovanni, Marco Genovese, Alan L. Migdall, M. G. Mingolla, Matteo G. A. Paris, Fabrizio Piacentini, Sergey V. Polyakov
Abstract
G. Brida, L. Ciavarella, Ivo Pietro Degiovanni, Marco Genovese, Alan L. Migdall, M. G. Mingolla, Matteo G. A. Paris, Fabrizio Piacentini, Sergey V. Polyakov
Abstract
A quantum measurement can be described by a set of matrices, one for each possible outcome, which represents the positive operator-valued measure (POVM) of the sensor. Efficient protocols of POVM extraction for arbitrary sensors are required. We present the first experimental POVM reconstruction that takes explicit advantage of a quantum resource, i.e., nonclassical correlations with an ancillary state. A POVM of a photon-number-resolving detector is reconstructed by using strong quantum correlations of twin beams generated by parametric down-conversion. Our reconstruction method is more statistically robust than POVM reconstruction methods that use classical input states.
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A quantum measurement can be described by a set of matrices, one for each possible outcome, which represents the positive operator-valued measure (POVM) of the sensor. Efficient protocols of POVM extraction for arbitrary sensors are required. We present the first experimental POVM reconstruction that takes explicit advantage of a quantum resource, i.e., nonclassical correlations with an ancillary state. A POVM of a photon-number-resolving detector is reconstructed by using strong quantum correlations of twin beams generated by parametric down-conversion. Our reconstruction method is more statistically robust than POVM reconstruction methods that use classical input states.
Key concepts: POVM, Measure (data warehouse), Parametric statistics, Detector, Operator (biology), Set (abstract data type), Calibration, Mathematics