Quantum state estimation with unknown measurements

Improved measurement techniques are central to technological development and foundational scientific exploration. Quantum optics relies upon detectors sensitive to non-classical features of light, enabling precise tests of physical laws and quantum-enhanced technologies such as precision measurement and secure communications. Accurate detector response calibration for quantum-scale inputs is key to future research and development in these cognate areas. To address this requirement quantum detector tomography (QDT) has been recently introduced. However, the QDT approach becomes increasingly challenging as the complexity of the detector response and input space grows. Here we present the first experimental implementation of a versatile alternative characterization technique to address many-outcome quantum detectors by limiting the input calibration region. To demonstrate the applicability of this approach the calibrated detector is subsequently used to estimate non-classical photon number states.Comment: 7 pages, 3 figure

Rank-based camera spectral sensitivity estimation

In order to accurately predict a digital camera response to spectral stimuli, the spectral sensitivity functions of its sensor need to be known. These functions can be determined by direct measurement in the lab—a difficult and lengthy procedure—or through simple statistical inference. Statistical inference methods are based on the observation that when a camera responds linearly to spectral stimuli, the device spectral sensitivities are linearly related to the camera rgb response values, and so can be found through regression. However, for rendered images, such as the JPEG images taken by a mobile phone, this assumption of linearity is violated. Even small departures from linearity can negatively impact the accuracy of the recovered spectral sensitivities, when a regression method is used. In our work, we develop a novel camera spectral sensitivity estimation technique that can recover the linear device spectral sensitivities from linear images and the effective linear sensitivities from rendered images. According to our method, the rank order of a pair of responses imposes a constraint on the shape of the underlying spectral sensitivity curve (of the sensor). Technically, each rank-pair splits the space where the underlying sensor might lie in two parts (a feasible region and an infeasible region). By intersecting the feasible regions from all the ranked-pairs, we can find a feasible region of sensor space. Experiments demonstrate that using rank orders delivers equal estimation to the prior art. However, the Rank-based method delivers a step-change in estimation performance when the data is not linear and, for the first time, allows for the estimation of the effective sensitivities of devices that may not even have “raw mode.” Experiments validate our method

Resource use in a low-input organic vegetable food supply system in UK - a case study

Use of local renewable resources in a low-input organic vegetable food supply system in UK is evaluated against the use of imported resources in the same system. Despite much focus by the farmer on low-input, the production and distribution system is only supported by 13% local renewable resources based on an emergy assessment. Future sustainability of such systems are discussed