arXiv subjects

Q. Ai

Publications and source records attributed to Q. Ai.

4 recordsLinked to original sources

arXiv2025

Searching Axion-like Dark Matter by Amplifying Weak Magnetic Field with Quantum Zeno effect

The enhancement of weak signals and the detection of hypothetical particles, facilitated by quantum amplification, are crucial for advancing fundamental physics and its practical applications. Recently, it was experimentally observed that magnetic field can be amplified by using nuclear spins under Markovian noise, [H. Su, et al., Phys. Rev. Lett. 133, 191801 (2024)]. Here, we theoretically propose amplifying the magnetic-field signal by using nuclear spins by the quantum Zeno effect (QZE). Under identical conditions, we demonstrate that compared to the Markovian case the amplification of the weak magnetic field can be enhanced by a factor about $e^{1/2}$ under a Gaussian noise. Moreover, through numerical simulations we determine the optimal experimental parameters for amplification conditions. This work shows that the combination of the QZE and spin amplification effectively enhances the amplification of the weak magnetic field. Our findings may provide valuable guidance for the design of experiments on establishing new constraints of dark matter and exotic interactions in the near future.

quant-ph↗

arXiv2015

A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory

Model-based analysis tools, built on assumptions and simplifications, are difficult to handle smart grids with data characterized by 4Vs data. This paper, using random matrix theory (RMT), motivates data-driven tools to perceive the complex grids in highdimension; meanwhile, an architecture with detailed procedures is proposed. In algorithm perspective, the architecture performs a high-dimensional analysis, and compares the findings with RMT predictions to conduct anomaly detections. Mean Spectral Radius (MSR), as a statistical indicator, is defined to reflect the correlations of system data in different dimensions. In management mode perspective, a group-work mode is discussed for smart grids operation. This mode breaks through regional limitations for energy flows and data flows, and makes advanced big data analyses possible. For a specific large-scale zone-dividing system with multiple connected utilities, each site, operating under the group-work mode, is able to work out the regional MSR only with its own measured/simulated data. The large-scale interconnected system, in this way, is naturally decoupled from statistical parameters perspective, rather than from engineering models perspective. Furthermore, a comparative analysis of these distributed MSRs, even with imperceptible different raw data, will produce a contour line to detect the event and locate the source. It demonstrates that the architecture is compatible with the block calculation only using the regional small database; beyond that, this architecture, as a data-driven solution, is sensitive to system situation awareness, and practical for real large-scale interconnected systems. Five case studies and their visualizations validate the designed architecture in various fields of power systems. To our best knowledge, this study is the first attempt to apply big data technology into smart grids.

stat.ME↗

arXiv2015

A Random Matrix Theoretical Approach to Early Event Detection Using Experimental Data

In this paper, High-dimensional data analysis methods are proposed to deal with random matrix which is composed by the real data from power network before and after the fault. The mean spectral radius (MSR) of non-Hermitian random matrices is defined as a statistic analytic for the fault detection. By analyzing the characteristics of random matrices and observing the changes of the spectral radius of random matrices, grid failure detection will be achieved. This paper describes the basic mathematical theory of this big data method, and the real-world data of a certain China power grid is used to verify the methods.

stat.AP↗

arXiv2010

Decoherence-Based Quantum Zeno Effect in a Cavity-QED System

We present a decoherence-based interpretation for the quantum Zeno effect (QZE) where measurements are dynamically treated as dispersive couplings of the measured system to the apparatus, rather than the von Neumann's projections. It is found that the explicit dependence of the survival probability on the decoherence time quantitatively distinguishes this dynamic QZE from the usual one based on projection measurements. By revisiting the cavity-QED experiment of the QZE [J. Bernu, et al., Phys. Rev. Lett, 101, 180402 (2008)], we suggest an alternative scheme to verify our theoretical consideration that frequent measurements slow down the increase of photon number inside a microcavity due to the nondemolition couplings with the atoms in large detuning.

quant-ph↗