Deep reinforcement learning for quantum multiparameter estimation

Estimation of physical quantities is at the core of most scientific research, and the use of quantum devices promises to enhance its performances. In real scenarios, it is fundamental to consider that resources are limited, and Bayesian adaptive estimation represents a powerful approach to efficiently allocate, during the estimation process, all the available resources. However, this framework relies on the precise knowledge of the system model, retrieved with a fine calibration, with results that are often computationally and experimentally demanding. We introduce a model-free and deep-learning-based approach to efficiently implement realistic Bayesian quantum metrology tasks accomplishing all the relevant challenges, without relying on any a priori knowledge of the system. To overcome this need, a neural network is trained directly on experimental data to learn the multiparameter Bayesian update. Then the system is set at its optimal working point through feedback provided by a reinforcement learning algorithm trained to reconstruct and enhance experiment heuristics of the investigated quantum sensor. Notably, we prove experimentally the achievement of higher estimation performances than standard methods, demonstrating the strength of the combination of these two black-box algorithms on an integrated photonic circuit. Our work represents an important step toward fully artificial intelligence-based quantum metrology

Experimental multiparameter quantum metrology in adaptive regime

Relevant metrological scenarios involve the simultaneous estimation of multiple parameters. The fundamental ingredient to achieve quantum-enhanced performances is based on the use of appropriately tailored quantum probes. However, reaching the ultimate resolution allowed by physical laws requires non trivial estimation strategies both from a theoretical and a practical point of view. A crucial tool for this purpose is the application of adaptive learning techniques. Indeed, adaptive strategies provide a flexible approach to obtain optimal parameter-independent performances, and optimize convergence to the fundamental bounds with limited amount of resources. Here, we combine on the same platform quantum-enhanced multiparameter estimation attaining the corresponding quantum limit and adaptive techniques. We demonstrate the simultaneous estimation of three optical phases in a programmable integrated photonic circuit, in the limited resource regime. The obtained results show the possibility of successfully combining different fundamental methodologies towards transition to quantum sensors applications

Aerosol Data Sources and Their Roles within PARAGON

We briefly but systematically review major sources of aerosol data, emphasizing suites of measurements that seem most likely to contribute to assessments of global aerosol climate forcing. The strengths and limitations of existing satellite, surface, and aircraft remote sensing systems are described, along with those of direct sampling networks and ship-based stations. It is evident that an enormous number of aerosol-related observations have been made, on a wide range of spatial and temporal sampling scales, and that many of the key gaps in this collection of data could be filled by technologies that either exist or are expected to be available in the near future. Emphasis must be given to combining remote sensing and in situ active and passive observations and integrating them with aerosol chemical transport models, in order to create a more complete environmental picture, having sufficient detail to address current climate forcing questions. The Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) initiative would provide an organizational framework to meet this goal

On requirements for a satellite mission to measure tropical rainfall

Tropical rainfall data are crucial in determining the role of tropical latent heating in driving the circulation of the global atmosphere. Also, the data are particularly important for testing the realism of climate models, and their ability to simulate and predict climate accurately on the seasonal time scale. Other scientific issues such as the effects of El Nino on climate could be addressed with a reliable, extended time series of tropical rainfall observations. A passive microwave sensor is planned to provide information on the integrated column precipitation content, its areal distribution, and its intensity. An active microwave sensor (radar) will define the layer depth of the precipitation and provide information about the intensity of rain reaching the surface, the key to determining the latent heat input to the atmosphere. A visible/infrared sensor will provide very high resolution information on cloud coverage, type, and top temperatures and also serve as the link between these data and the long and virtually continuous coverage by the geosynchronous meteorological satellites. The unique combination of sensor wavelengths, coverages, and resolving capabilities together with the low-altitude, non-Sun synchronous orbit provide a sampling capability that should yield monthly precipitation amounts to a reasonable accuracy over a 500- by 500-km grid

Visualization of hydrogen injection in a scramjet engine by simultaneous PLIF imaging and laser holographic imaging

Flowfield characterization has been accomplished for several fuel injector configurations using simultaneous planar laser induced fluorescence (PLIF) and laser holographic imaging (LHI). The experiments were carried out in the GASL-NASA HYPULSE real gas expansion tube facility, a pulsed facility with steady test times of about 350 microsec. The tests were done at simulated Mach numbers 13.5 and 17. The focus of this paper is on the measurement technologies used and their application in a research facility. The HYPULSE facility, the models used for the experiments, and the setup for the LHI and PLIF measurements are described. Measurement challenges and solutions are discussed. Results are presented for experiments with several fuel injector configurations and several equivalence ratios

MarinEye - A tool for marine monitoring

This work presents an autonomous system for marine integrated physical-chemical and biological monitoring – the MarinEye system. It comprises a set of sensors providing diverse and relevant information for oceanic environment characterization and marine biology studies. It is constituted by a physicalchemical water properties sensor suite, a water filtration and sampling system for DNA collection, a plankton imaging system and biomass assessment acoustic system. The MarinEye system has onboard computational and logging capabilities allowing it either for autonomous operation or for integration in other marine observing systems (such as Observatories or robotic vehicles. It was designed in order to collect integrated multi-trophic monitoring data. The validation in operational environment on 3 marine observatories: RAIA, BerlengasWatch and Cascais on the coast of Portugal is also discussed.info:eu-repo/semantics/publishedVersio

Calibration of multiparameter sensors via machine learning at the single-photon level

Calibration of sensors is a fundamental step in validating their operation. This can be a demanding task, as it relies on acquiring detailed modeling of the device, which can be aggravated by its possible dependence upon multiple parameters. Machine learning provides a handy solution to this issue, operating a mapping between the parameters and the device response, without needing additional specific information on its functioning. Here, we demonstrate the application of a neural-network-based algorithm for the calibration of integrated photonic devices depending on two parameters. We show that a reliable characterization is achievable by carefully selecting an appropriate network training strategy. These results show the viability of this approach as an effective tool for the multiparameter calibration of sensors characterized by complex transduction functions. Furthermore, the approach is proven to be versatile and promising for mass production, as the same neural network is able to calibrate different devices that have the same structure

Research and development of optical measurement techniques for aerospace propulsion research: A NASA Lewis Research Center perspective

The applied research effort required to develop new nonintrusive measurement techniques capable of obtaining the data required by aerospace propulsion researchers and of operating in the harsh environments encountered in research and test facilities is discussed and illustrated through several ongoing projects at NASA's Lewis Research Center. Factors including length of development time, funding levels, and collaborative support from fluid-thermal researchers are cited. Progress in developing new instrumentation via a multi-path approach, including NASA research, grant, and government-sponsored research through mechanisms like the Small Business Innovative Research program, is also described