Single trusted qubit is necessary and sufficient for quantum realisation of extremal no-signaling correlations

Quantum statistics can be considered from the perspective of postquantum no-signaling theories in which either none or only a certain number of quantum systems are trusted. In these scenarios, the role of states is played by the so-called no-signaling boxes or no-signaling assemblages respectively. It has been shown so far that in the usual Bell non-locality scenario with a single measurement run, quantum statistics can never reproduce an extremal non-local point within the set of no-signaling boxes. We provide here a general no-go rule showing that the latter stays true even if arbitrary sequential measurements are allowed. On the other hand, we prove a positive result showing that already a single trusted qubit is enough for quantum theory to produce a self-testable extremal point within the corresponding set of no-signaling assemblages. This result opens up the possibility for security proofs of cryptographic protocols against general no-signaling adversaries.Comment: 14 page

The communication cost of simulating POVMs over maximally entangled qubits

In [Toner and Bacon, Phys. Rev. Lett. 91, 187904 (2003)], $1$ bit of communication was proven to be enough to simulate the statistics of local projective measurements over the maximally entangled state. Ever since then, the question of whether $1$ bit is also enough for the case of generalized measurements has been open. In this thesis, we retort to inefficiency-resistant Bell functionals, a powerful technique to prove lower bounds communication complexity, to numerically study this question. The results obtained suggest that, indeed, as is the case with projective measurements, $1$ bit of communication suffices to simulate POVMs over maximally entangled qubits

Dense Crowds Detection and Surveillance with Drones using Density Maps

Detecting and Counting people in a human crowd from a moving drone present challenging problems that arisefrom the constant changing in the image perspective andcamera angle. In this paper, we test two different state-of-the-art approaches, density map generation with VGG19 trainedwith the Bayes loss function and detect-then-count with FasterRCNN with ResNet50-FPN as backbone, in order to comparetheir precision for counting and detecting people in differentreal scenarios taken from a drone flight. We show empiricallythat both proposed methodologies perform especially well fordetecting and counting people in sparse crowds when thedrone is near the ground. Nevertheless, VGG19 provides betterprecision on both tasks while also being lighter than FasterRCNN. Furthermore, VGG19 outperforms Faster RCNN whendealing with dense crowds, proving to be more robust toscale variations and strong occlusions, being more suitable forsurveillance applications using dronesComment: 2020 International Conference on Unmanned Aircraft Systems (ICUAS), Athens, Greece, 202

Implementation of a Natural User Interface to Command a Drone

In this work, we propose the use of a Natural User Interface (NUI) through body gestures using the open source library OpenPose, looking for a more dynamic and intuitive way to control a drone. For the implementation, we use the Robotic Operative System (ROS) to control and manage the different components of the project. Wrapped inside ROS, OpenPose (OP) processes the video obtained in real-time by a commercial drone, allowing to obtain the user's pose. Finally, the keypoints from OpenPose are obtained and translated, using geometric constraints, to specify high-level commands to the drone. Real-time experiments validate the full strategy.Comment: 2020 International Conference on Unmanned Aircraft Systems (ICUAS), Athens, Greece, 202

On the existence of unbiased resilient estimators in discrete quantum systems

Cram\'er-Rao constitutes a crucial lower bound for the mean squared error of an estimator in frequentist parameter estimation, albeit paradoxically demanding highly accurate prior knowledge of the parameter to be estimated. Indeed, this information is needed to construct the optimal unbiased estimator, which is highly dependent on the parameter. Conversely, Bhattacharyya bounds result in a more resilient estimation about prior accuracy by imposing additional constraints on the estimator. Initially, we conduct a quantitative comparison of the performance between Cram\'er-Rao and Bhattacharyya bounds when faced with less-than-ideal prior knowledge of the parameter. Furthermore, we demonstrate that the $n^{th}$order classical and quantum Bhattacharyya bounds cannot be computed -- given the absence of estimators satisfying the constraints -- under specific conditions tied to the dimension $m$ of the discrete system. Intriguingly, for a system with the same dimension $m$, the maximum non-trivial order $n$ is $m-1$ in the classical case, while in the quantum realm, it extends to $m(m+1)/2-1$. Consequently, for a given system dimension, one can construct estimators in quantum systems that exhibit increased robustness to prior ignorance.Comment: typos corrected and we extended some explanation

Monitoring Social-distance in Wide Areas during Pandemics: a Density Map and Segmentation Approach

With the relaxation of the containment measurements around the globe, monitoring the social distancing in crowded public places is of grate importance to prevent a new massive wave of COVID-19 infections. Recent works in that matter have limited themselves by detecting social distancing in corridors up to small crowds by detecting each person individually considering the full body in the image. In this work, we propose a new framework for monitoring the social-distance using end-to-end Deep Learning, to detect crowds violating the social-distance in wide areas where important occlusions may be present. Our framework consists in the creation of a new ground truth based on the ground truth density maps and the proposal of two different solutions, a density-map-based and a segmentation-based, to detect the crowds violating the social-distance constrain. We assess the results of both approaches by using the generated ground truth from the PET2009 and CityStreet datasets. We show that our framework performs well at providing the zones where people are not following the social-distance even when heavily occluded or far away from one camera.Comment: Video: https://youtu.be/TwzBMKg7h_