Quantum circuit fidelity is a crucial metric for assessing the accuracy of quantum computation results and indicating the precision of quantum algorithm execution. The primary methods for assessing quantum circuit fid...
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Quantum circuit fidelity is a crucial metric for assessing the accuracy of quantum computation results and indicating the precision of quantum algorithm execution. The primary methods for assessing quantum circuit fidelity include direct fidelity estimation and mirror circuit fidelity estimation. The former is challenging to implement in practice, while the latter requires substantial classical computational resources and numerous experimental runs. In this paper, we propose a fidelity estimation method based on Layer Interleaved Randomized Benchmarking, which decomposes a complex quantum circuit into multiple sublayers. By independently evaluating the fidelity of each layer, one can comprehensively assess the performance of the entire quantum circuit. This layered evaluation strategy not only enhances accuracy but also effectively identifies and analyzes errors in specific quantum gates or qubits through independent layer evaluation. Simulation results demonstrate that the proposed method improves circuit fidelity by an average of 6.8% and 4.1% compared to Layer Randomized Benchmarking and Interleaved Randomized Benchmarking methods in a thermal relaxation noise environment, and by 40% compared to Layer RB in a bit-flip noise environment. Moreover, the method detects preset faulty quantum gates in circuits generated by the Munich Quantum Toolkit Benchmark, verifying the model’s validity and providing a new tool for faulty gate detection in quantum circuits.
The rapid development of the Internet of Things (IoT) has led to an increasing demand for symmetric ciphers in IoT devices. Shadow is a lightweight block cipher proposed in 2021. In previous research, the security eva...
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Multilingual automatic speech recognition represents a crucial research direction in tackling the challenges associated with low-resource scenarios. To effectively incorporate language-specific information in the join...
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Rumors on social media can cause panic during emergency events. Unlike conventional rumor detection, it is more challenging to detect rumors about emergency events that have not happened in history, due to the shortag...
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The audio-visual event localization task investigates how audio and visual modalities can mutually enhance video event localization. Current methods often rely on single-modality features or lack effective initial ali...
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Electronic Design Automation (EDA) plays a crucial role in classical chip design and significantly influences the development of quantum chip design. However, traditional EDA tools cannot be directly applied to quantu...
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Anonymous messaging system allows users to deliver messages without revealing the sending content and their identifiers, which has attracted ongoing concerns. However, to the best of our knowledge, all the existing so...
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In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh env...
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In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.
In recent years, spiking neural networks (SNNs) have gained significant attention in visual recognition tasks due to the low computational energy. However, most SNNs have a large number of parameters, which limits the...
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Class-incremental learning (CIL) aims to learn a series of tasks sequentially, each introducing several new categories. Because providing the task labels during inference can significantly increase accuracy, many appr...
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