Statistical Inference with Quantum Measurements: Methodologies for Nitrogen Vacancy Centers in Diamond
arXiv:1705.10897 · doi:10.1088/1367-2630/aa9c9f
Abstract
The analysis of photon count data from the standard nitrogen vacancy (NV) measurement process is treated as a statistical inference problem. This has applications toward gaining better and more rigorous error bars for tasks such as parameter estimation (eg. magnetometry), tomography, and randomized benchmarking. We start by providing a summary of the standard phenomenological model of the NV optical process in terms of Lindblad jump operators. This model is used to derive random variables describing emitted photons during measurement, to which finite visibility, dark counts, and imperfect state preparation are added. NV spin-state measurement is then stated as an abstract statistical inference problem consisting of an underlying biased coin obstructed by three Poisson rates. Relevant frequentist and Bayesian estimators are provided, discussed, and quantitatively compared. We show numerically that the risk of the maximum likelihood estimator is well approximated by the Cramer-Rao bound, for which we provide a simple formula. Of the estimators, we in particular promote the Bayes estimator, owing to its slightly better risk performance, and straight-forward error propagation into more complex experiments. This is illustrated on experimental data, where Quantum Hamiltonian Learning is performed and cross-validated in a fully Bayesian setting, and compared to a more traditional weighted least squares fit.
41 pages and 14 figures, including the appendices
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- Learning Quantum Systems
- Quantum diamond spectrometer for nanoscale NMR and ESR spectroscopy
- Learning models of quantum systems from experiments
- Bayesian estimation for quantum sensing in the absence of single-shot detection
- Magnetic-field-learning using a single electronic spin in diamond with one-photon-readout at room temperature
- Adversarial Hamiltonian learning of quantum dots in a minimal Kitaev chain
- Fast Quantum State Tomography in the Nitrogen Vacancy Center of Diamond
- Low-frequency quantum sensing
- Hamiltonian learning with real-space impurity tomography in topological moire superconductors
- Machine learning the Kondo entanglement cloud from local measurements
- Noisy Coherent Population Trapping: Applications to Noise Estimation and Qubit State Preparation
- Nuclear spin relaxation in solid state defect quantum bits via electron-phonon coupling in their optical excited state