Datadriven inference, reconstruction, and observational completeness of quantum devices
Abstract
The range of a quantum measurement is the set of outcome probability distributions that can be produced by varying the input state. We introduce datadriven inference as a protocol that, given a set of experimental data as a collection of outcome distributions, infers the quantum measurement which is (i) consistent with the data, in the sense that its range contains all the distributions observed, and (ii) maximally noncommittal, in the sense that its range is of minimum volume in the space of outcome distributions. We show that datadriven inference is able to return a unique measurement for any data set if and only if the inference adopts a (hyper)spherical state space (for example, the classical or the quantum bit). In analogy to informational completeness for quantum tomography, we define observational completeness as the property of any set of states that, when fed into any given measurement, produces a set of outcome distributions allowing for the correct reconstruction of the measurement via datadriven inference. We show that observational completeness is strictly stronger than informational completeness, in the sense that not all informationally complete sets are also observationally complete. Moreover, we show that for systems with a (hyper)spherical state space, the only observationally complete simplex is the regular one, namely, the symmetric informationally complete set.
 Publication:

Physical Review A
 Pub Date:
 December 2020
 DOI:
 10.1103/PhysRevA.102.062407
 arXiv:
 arXiv:1905.04895
 Bibcode:
 2020PhRvA.102f2407D
 Keywords:

 Quantum Physics
 EPrint:
 9 pages, 5 figures