Pauli Tomography: complete characterization of a single qubit device

The marriage of Quantum Physics and Information Technology, originally motivated by the need for miniaturization, has recently opened the way to the realization of radically new information-processing devices, with the possibility of guaranteed secure cryptographic communications, and tremendous speedups of some complex computational tasks. Among the many problems posed by the new information technology there is the need of characterizing the new quantum devices, making a complete identification and characterization of their functioning. As we will see, quantum mechanics provides us with a powerful tool to achieve the task easily and efficiently: this tools is the so called quantum entanglement, the basis of the quantum parallelism of the future computers. We present here the first full experimental quantum characterization of a single-qubit device. The new method, we may refer to as ''quantum radiography'', uses a Pauli Quantum Tomography at the output of the device, and needs only a single entangled state at the input, which works on the test channel as all possible input states in quantum parallel. The method can be easily extended to any n-qubits device

Bioethanol in biofuels checked by an amperometric organic phase enzyme electrode (OPEE) working in “substrate antagonism” format

The bioethanol content of two samples of biofuels was determined directly, after simple dilution in decane, by means of an amperometric catalase enzyme biosensor working in the organic phase, based on substrate antagonisms format. The results were good from the point of view of accuracy, and satisfactory for what concerns the recovery test by the standard addition method. Limit of detection (LOD) was on the order of 2.5 × 10−5 M. © 2016 by the authors; licensee MDPI, Basel, Switzerland

Radioluminescence of synthetic and natural quartz

The effect of X-ray irradiation and thermal treatments on the radio-luminescence emission spectrum of both a natural pegmatitic quartz and a synthetic one was investigated. All the emission spectra could be deconvolved into the same set of five Gaussian components. Among the identified RL bands, a blue emission at 2.53 eV (480 nm) is enhanced under X-ray irradiation. A strong correlation with the sensitization of the so called "110 degrees C" TSL peak (in our measurements seen at lower temperature due to the lower heating rate) was proved, suggesting that the recombination centers associated with the 2.53 eV band are produced under X-ray irradiation and are involved in both RL and TSL luminescence mechanisms. When each irradiation was followed by heating up to 500 degrees C a strong sensitization of the RL band emitting at 3.44 eV and of the 110 degrees C TSL peak were observed. A perfect correlation between the RL and TSL emissions suggests that the recombination centers involved in the RL and TSL emissions are the sam

Human-Centered Navigation and Person-Following with Omnidirectional Robot for Indoor Assistance and Monitoring

Robot assistants and service robots are rapidly spreading out as cutting-edge automation solutions to support people in their everyday life in workplaces, health centers, and domestic environments. Moreover, the COVID-19 pandemic drastically increased the need for service technology to help medical personnel in critical conditions in hospitals and domestic scenarios. The first requirement for an assistive robot is to navigate and follow the user in dynamic environments in complete autonomy. However, these advanced multitask behaviors require flexible mobility of the platform to accurately avoid obstacles in cluttered spaces while tracking the user. This paper presents a novel human-centered navigation system that successfully combines a real-time visual perception system with the mobility advantages provided by an omnidirectional robotic platform to precisely adjust the robot orientation and monitor a person while navigating. Our extensive experimentation conducted in a representative indoor scenario demonstrates that our solution offers efficient and safe motion planning for person-following and, more generally, for human-centered navigation tasks

Comparison between a direct-flow SPR immunosensor for ampicillin and a competitive conventional amperometric device: analytical features and possible applications to real samples

In this research, we developed a direct-flow surface plasmon resonance (SPR) immunosensor for ampicillin to perform direct, simple, and fast measurements of this important antibiotic. In order to better evaluate the performance, it was compared with a conventional amperometric immunosensor, working with a competitive format with the aim of finding out experimental real advantages and disadvantages of two respective methods. Results showed that certain analytical features of the new SPR immunodevice, such as the lower limit of detection (LOD) value and the width of the linear range, are poorer than those of a conventional amperometric immunosensor, which adversely affects the application to samples such as natural waters. On the other hand, the SPR immunosensor was more selective to ampicillin, and measurements were more easily and quickly attained compared to those performed with the conventional competitive immunosensor

RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring

Robot assistants are emerging as high-tech solutions to support people in everyday life. Following and assisting the user in the domestic environment requires flexible mobility to safely move in cluttered spaces. We introduce a new approach to person following for assistance and monitoring. Our methodology exploits an omnidirectional robotic platform to detach the computation of linear and angular velocities and navigate within the domestic environment without losing track of the assisted person. While linear velocities are managed by a conventional Dynamic Window Approach (DWA) local planner, we trained a Deep Reinforcement Learning (DRL) agent to predict optimized angular velocities commands and maintain the orientation of the robot towards the user. We evaluate our navigation system on a real omnidirectional platform in various indoor scenarios, demonstrating the competitive advantage of our solution compared to a standard differential steering following

Exploiting quantum parallelism of entanglement for a complete experimental quantum characterization of a single qubit device

We present the first full experimental quantum tomographic characterization of a single-qubit device achieved with a single entangled input state. The entangled input state plays the role of all possible input states in quantum parallel on the tested device. The method can be trivially extended to any n-qubits device by just replicating the whole experimental setup n times.Comment: 4 pages in revtex4 with 4 eps figure

PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning

Learning agents can optimize standard autonomous navigation improving flexibility, efficiency, and computational cost of the system by adopting a wide variety of approaches. This work introduces the \textit{PIC4rl-gym}, a fundamental modular framework to enhance navigation and learning research by mixing ROS2 and Gazebo, the standard tools of the robotics community, with Deep Reinforcement Learning (DRL). The paper describes the whole structure of the PIC4rl-gym, which fully integrates DRL agent's training and testing in several indoor and outdoor navigation scenarios and tasks. A modular approach is adopted to easily customize the simulation by selecting new platforms, sensors, or models. We demonstrate the potential of our novel gym by benchmarking the resulting policies, trained for different navigation tasks, with a complete set of metrics

Domain Generalization for Crop Segmentation with Knowledge Distillation

In recent years, precision agriculture has gradually oriented farming closer to automation processes to support all the activities related to field management. Service robotics plays a predominant role in this evolution by deploying autonomous agents that can navigate fields while performing tasks without human intervention, such as monitoring, spraying, and harvesting. To execute these precise actions, mobile robots need a real-time perception system that understands their surroundings and identifies their targets in the wild. Generalizing to new crops and environmental conditions is critical for practical applications, as labeled samples are rarely available. In this paper, we investigate the problem of crop segmentation and propose a novel approach to enhance domain generalization using knowledge distillation. In the proposed framework, we transfer knowledge from an ensemble of models individually trained on source domains to a student model that can adapt to unseen target domains. To evaluate the proposed method, we present a synthetic multi-domain dataset for crop segmentation containing plants of variegate shapes and covering different terrain styles, weather conditions, and light scenarios for more than 50,000 samples. We demonstrate significant improvements in performance over state-of-the-art methods and superior sim-to-real generalization. Our approach provides a promising solution for domain generalization in crop segmentation and has the potential to enhance a wide variety of precision agriculture applications

Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation

Single-Image Super-Resolution can support robotic tasks in environments where a reliable visual stream is required to monitor the mission, handle teleoperation or study relevant visual details. In this work, we propose an efficient Generative Adversarial Network model for real-time Super-Resolution. We adopt a tailored architecture of the original SRGAN and model quantization to boost the execution on CPU and Edge TPU devices, achieving up to 200 fps inference. We further optimize our model by distilling its knowledge to a smaller version of the network and obtain remarkable improvements compared to the standard training approach. Our experiments show that our fast and lightweight model preserves considerably satisfying image quality compared to heavier state-of-the-art models. Finally, we conduct experiments on image transmission with bandwidth degradation to highlight the advantages of the proposed system for mobile robotic applications