This study provides insights into the current limitations of quantum machine learning compared to classical machine learning and identifies areas for future research. We present a novel approach that utilizes real IBM quantum computers to classify celestial objects within the extensive Sloan Digital Sky Survey Data Release 18 (SDSS-V DR18) dataset. Despite persistent challenges in both hardware and software, quantum computers are being explored as tools for enhancing machine learning performance in comparison to classical methods due to potential upside. This investigation delves into the untapped potential of quantum machine learning and quantum neural networks in tackling the complexities of processing vast telescope data. By leveraging quantum technologies, we aim to expedite the analysis of large complex data, unveiling hidden patterns, and propelling specialized fields such as astronomical research into the quantum era.
Network traffic has increased substantially due to the introduction of advanced network-enabled applications and devices. The introduction of software defined networks (SDNs) and machine learning (ML) has empowered optimizing network operations and network traffic monitoring, resulting in improved complex traffic operations and security with faster malicious intention detections. This paper focuses on network traffic data collection systems, and the data is evaluated using a survey of ML algorithms, depending on the data type (tabular or image). Adhering to system architecture best practices including a decoupled design to integrate with existing network monitoring infrastructures and cybersecurity standards; and online and offline data collection via packet capture (PCAP) standards. For packet based network traffic data analysis, we convert captured data into images and feed into a convolutional neural network to classify the data based on requirements. For statistical based network traffic data analysis, we apply feature engineering on tabular data and feed into various ML systems to classify based on requirements. Finally, We show that the same ML algorithm outperforms publicly available datasets using our collection method.
Recent advances in quantum machine learning and quantum state embedding are integrated, providing a resource efficient framework for solutions of linear systems on Noisy Intermediate Scale Quantum (NISQ) machines. A divide and conquer algorithm is used to embed the indexing vector after which the Coherent Variational Quantum Linear Solver (CVQLS) algorithm is used to invert the problem matrix. This integrated procedure has an improved complexity scaling in the quantum resources needed to execute and produces solutions which agree with what is found classically.
In this report, the characteristics of non-line-of-sight (NLOS) ultraviolet (UV) overwater communication channels which includes scattering and turbulence effects are modeled using Monte Carlo multiple scattering simulation. The overwater field experimental measurements of turbulence effect and path loss at the distances of up to 1700 meters were reported and analyzed by considering key parameters such as communication distance, transceiver angles, water surface reflection index, along with airborne humidity, and temperature. These channel modeling and experimental results will serve as the foundation for further study of the NLOS UV overwater communication system in shore-to-shore (STS), shore-to-vessel (STV), and vessel-to-vessel (VTV) communication link configurations.
We developed a three-photon adaptive optics add-on to a commercial two-photon laser scanning microscope. We
demonstrated its capability for structural and functional imaging of neurons labeled with genetically encoded red
fluorescent proteins or calcium indicators deep in the living mouse brain with cellular and subcellular resolution.
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