130 research outputs found

    Minimal Clifford Shadow Estimation by Mutually Unbiased Bases

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    Predicting properties of large-scale quantum systems is crucial for the development of quantum science and technology. Shadow estimation is an efficient method for this task based on randomized measurements, where many-qubit random Clifford circuits are used for estimating global properties like quantum fidelity. Here we introduce the minimal Clifford measurement (MCM) to reduce the number of possible random circuits to the minimum, while keeping the effective post-processing channel in shadow estimation. In particular, we show that MCM requires 2n+12^n+1 distinct Clifford circuits, and it can be realized by Mutually Unbiased Bases (MUB), with nn as the total qubit number. By applying the Z-Tableau formalism, this ensemble of circuits can be synthesized to the SCZH\mathrm{-S-CZ-H-} structure, which can be composed by 2n12n-1 \emph{fixed} circuit modules, and the total circuit depth is at most n+1n+1. Compared to the original Clifford measurements, our MCM significantly reduces the circuit complexity and the compilation costs. In addition, we find the sampling advantage of MCM on estimating off-diagonal operators, and extend this observation to the biased-MCM scheme to enhance the sampling improvement further.Comment: 10+15 pages, 9 figures. Comments are welcom

    DMAT: A Dynamic Mask-Aware Transformer for Human De-occlusion

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    Human de-occlusion, which aims to infer the appearance of invisible human parts from an occluded image, has great value in many human-related tasks, such as person re-id, and intention inference. To address this task, this paper proposes a dynamic mask-aware transformer (DMAT), which dynamically augments information from human regions and weakens that from occlusion. First, to enhance token representation, we design an expanded convolution head with enlarged kernels, which captures more local valid context and mitigates the influence of surrounding occlusion. To concentrate on the visible human parts, we propose a novel dynamic multi-head human-mask guided attention mechanism through integrating multiple masks, which can prevent the de-occluded regions from assimilating to the background. Besides, a region upsampling strategy is utilized to alleviate the impact of occlusion on interpolated images. During model learning, an amodal loss is developed to further emphasize the recovery effect of human regions, which also refines the model's convergence. Extensive experiments on the AHP dataset demonstrate its superior performance compared to recent state-of-the-art methods

    Reliability analysis of the impact of thickness measurement accuracy on the longitudinal strength assessment of CAP

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    The reliability analysis of the longitudinal bending strength of ships is conducted using the total probability method, taking into account and analyzing the accuracy error in thickness measurement of transverse section members on existing servicing ships. A mathematical model for calculating failure probability CAP ratings is established. By calculating the CAP rating of an aged LPG carrier, it is observed that when the deterministic method reaches the target rating, the failure probability in rating calculation by the reliability analysis method is high. This indicates a significant influence of thickness measurement accuracy on CAP rating accuracy
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