Lattice gauge theories, which originated from particle physics in the context of Quantum Chromodynamics (QCD), provide an important intellectual stimulus to further develop quantum information technologies. While one long-term goal is the reliable quantum simulation of currently intractable aspects of QCD itself, lattice gauge theories also play an important role in condensed matter physics and in quantum information science. In this way, lattice gauge theories provide both motivation and a framework for interdisciplinary research towards the development of special purpose digital and analog quantum simulators, and ultimately of scalable universal quantum computers. In this manuscript, recent results and new tools from a quantum science approach to study lattice gauge theories are reviewed. Two new complementary approaches are discussed: first, tensor network methods are presented – a classical simulation approach – applied to the study of lattice gauge theories together with some results on Abelian and non-Abelian lattice gauge theories. Then, recent proposals for the implementation of lattice gauge theory quantum simulators in different quantum hardware are reported, e.g., trapped ions, Rydberg atoms, and superconducting circuits. Finally, the first proof-of-principle trapped ions experimental quantum simulations of the Schwinger model are reviewed.

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Simulating Lattice Gauge Theories within Quantum Technologies

Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to perform, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies.

https://iopscience.iop.org/article/10.1088/1361-6579/ab299e/pdf

Recent development of respiratory rate measurement technologies

Information and operational technologies merge into the so-called industrial Internet of Things, which is one of the basic pillars of the Industry 4.0 paradigm. Roughly speaking, yet-to-come services will be offered in the automation scenario by industrial devices having an internet connection for sharing data in the cloud. Currently, most efforts are in the development of protocols able to ensure horizontal interoperability among heterogeneous applications. Consequently, poor attention is devoted to time-related performance. In this paper, a new, full software, platform-independent approach is proposed for experimentally evaluating the delay in transferring information across local and intercontinental routes by applications leveraging on messaging middleware. The application is realized using the node-RED web-based framework, due to its availability on different platforms; the widely accepted message queue telemetry transport protocol has been chosen thanks to its low overhead and complexity. For sake of completeness, five different, private and public, brokers are used. The adopted industrial-grade hardware, complemented by global positioning system time reference, permits an overall synchronization and timestamping accuracy of a few milliseconds. The vast measurement campaign highlighted that, generally, quality of service (QoS) type 1 offers low end-to-end delay (average value less than 0.5 s) with reduced variability (0.1 s). However, the maximum end-to-end one-way delay ranges from 1 s for QoS 0 to less than 1.5 s for fully acknowledged QoS 2.

Delay Estimation of Industrial IoT Applications Based on Messaging Protocols

Low-cost air pollution wireless sensors are emerging in densely distributed networks that provide more spatial resolution than typical traditional systems for monitoring ambient air quality. This paper presents an air quality measurement system that is composed of a distributed sensor network connected to a cloud system forming a wireless sensor network (WSN). Sensor nodes are based on low-power ZigBee motes, and transmit field measurement data to the cloud through a gateway. An optimized cloud computing system has been implemented to store, monitor, process, and visualize the data received from the sensor network. Data processing and analysis is performed in the cloud by applying artificial intelligence techniques to optimize the detection of compounds and contaminants. This proposed system is a low-cost, low-size, and low-power consumption method that can greatly enhance the efficiency of air quality measurements, since a great number of nodes could be deployed and provide relevant information for air quality distribution in different areas. Finally, a laboratory case study demonstrates the applicability of the proposed system for the detection of some common volatile organic compounds, including: benzene, toluene, ethylbenzene, and xylene. Principal component analysis, a multilayer perceptron with backpropagation learning algorithm, and support vector machine have been applied for data processing. The results obtained suggest good performance in discriminating and quantifying the concentration of the volatile organic compounds.

Wireless Sensor Network Combined with Cloud Computing for Air Quality Monitoring

This paper proposes a wearable monitoring system for inspection in the framework of Industry 4.0. The instrument integrates augmented reality (AR) glasses with a noninvasive single-channel brain–computer interface (BCI), which replaces the classical input interface of AR platforms. Steady-state visually evoked potentials (SSVEP) are measured by a single-channel electroencephalography (EEG) and simple power spectral density analysis. The visual stimuli for SSVEP elicitation are provided by AR glasses while displaying the inspection information. The real-time metrological performance of the BCI is assessed by the receiver operating characteristic curve on the experimental data from 20 subjects. The characterization was carried out by considering stimulation times from 10.0 down to 2.0 s. The thresholds for the classification were found to be dependent on the subject and the obtained average accuracy goes from 98.9% at 10.0 s to 81.1% at 2.0 s. An inspection case study of the integrated AR-BCI device shows encouraging accuracy of about 80% of lab values.

A Wearable Brain–Computer Interface Instrument for Augmented Reality-Based Inspection in Industry 4.0

Environmental monitoring is extremely important to ensure a safe and wealthy life of both humans and artifacts. Monitoring requirements are extremely different depending on the environment, leading to ad hoc implementations that lack flexibility. This paper describes an implementation that can be adapted to many different applications and embeds the flexibility required to be deployed and upgraded without the necessity of arranging complex infrastructures. The solution is based on small autonomous wireless sensor nodes, small wireless receivers connected to the Internet, and a cloud architecture which provides data storage and delivery to remote clients. The solution permits supervisors on-site not only to have an immediate idea of the current situation by using their smart-phones but also to monitor remote sites through the Internet. All measurements are redundantly stored at different concentration levels to guarantee a safe back-trace and to provide quality assurance also in case of network failure or unavailability. The sensing nodes have small impact, with dimensions which can be of less than 2.5 cm $\times1.5$ cm when the nodes have to acquire only temperature and relative humidity, and a low cost that enables using them in a set-and-forget way for intervals in excess of one year.

Wireless Sensor Network for Distributed Environmental Monitoring

The atmospheric particulate matter is considered one of the most dangerous pollutants because of its effects on the climate and human health. Particulate concentration changes largely with spatial position and time, and thus, a distributed real-time monitoring would be mandatory, especially in densely populated areas. The proposed optical sampling system has a negligible cost with respect to the already available instruments and can be used for deploying a capillary particulate monitoring network thanks to its wireless capability based on the LoRa protocol. The proposed solution employs an optical method for the atmospheric particulate detection and the estimation of its concentration and size distribution. The air is sampled by a small pump which forces a known flux through a commercial glass-fiber filter, where the particulate is captured. A low-cost digital camera coupled with a multi-wavelength lighting system takes periodical photographs of the filter surface, and a small PC-on-single-board processes the acquired images in order to identify the particles and to estimate their size. The system can work unattended for a long time and transmit remotely measurement data with a typical range of few kilometers.

An Optical Sampling System for Distributed Atmospheric Particulate Matter

Cloud based sensor network for environmental monitoring

The development of an electrochemical dissolved oxygen (DO) sensor based on a novel Cu(II) complex-modified screen printed carbon electrode is reported. The voltammetric behavior of the modified electrode was investigated at different scan rates and oxygen concentrations in PBS (pH = 7). An increase of cathodic current (at about −0.4 vs. Ag/AgCl) with the addition of oxygen was observed. The modified Cu(II) complex electrode was demonstrated for the determination of DO in water using chronoamperometry. A small size and low power consumption home-made portable electrochemical analyzer based on custom electronics for sensor interfacing and operating in voltammetry and amperometry modes has been also designed and fabricated. Its performances in the monitoring of DO in water were compared with a commercial one.

Development of a Novel Cu(II) Complex Modified Electrode and a Portable Electrochemical Analyzer for the Determination of Dissolved Oxygen (DO) in Water

Nowadays, effective detection of gases at ppm and ppb level is of crucial importance in a wide range of applications, such as industrial processes, environmental monitoring, public security and medical investigation. Several sensor types have been developed in last decades, among them the metal oxide gas sensors are the most promising for low-cost and portable applications, where good sensitivity and selectivity, together with small size are important constraints. The proposed conductometric gas sensor has been manufactured depositing a Nb 2 O 5 thin film by plasma sputtering on a commercial alumina substrate with platinum interdigitated electrodes and heater which size is 3 mm × 6 mm with a thickness of 1 mm. The Nb 2 O 5 thin film has been characterized by FE-SEM and XPS analysis. The sensor performance towards several target gases have been evaluated employing an experimental setup specifically developed for the characterization of gas sensors. The sensor exhibits good sensitivity towards acetone, a bio-marker found in human breath of diabetes patients. This makes the sensor promising in the noninvasive diagnosis of this kind of disease.

Luca Lombardo

Papers

Wireless Sensor Network for Distributed Environmental Monitoring

An Optical Sampling System for Distributed Atmospheric Particulate Matter

Cloud based sensor network for environmental monitoring

Development of a Novel Cu(II) Complex Modified Electrode and a Portable Electrochemical Analyzer for the Determination of Dissolved Oxygen (DO) in Water

Nb 2 O 5 thin film-based conductometric sensor for acetone monitoring