Holonomic techniques for massive 3-loop form factors: the gluonic case
研究使用高级计算机代数、自动化猜测和大规模PSLQ搜索解决了复杂胶子情况下的三圈形式因子计算问题。
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Selection, Representation, and Execution in Sparse Fourier Neural Operators
研究通过区分稀疏表示、存储参数、理论操作数和测量运行时间，探索了稀疏Fourier神经算子的不同实现路径，以解决模型大小和推理成本问题。
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Differentiable Jitter Correction using Deep Learning-based Image Quality Metric for Phase-Contrast Micro-CT
本文提出了一种基于深度学习图像质量度量的全可微抖动校正方法，用于X射线相衬显微CT，直接从投影数据估计和补偿每个投影的刚性抖动。
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A neural operator view on U-Nets for inverse imaging problems
This study addresses the challenge that increasing resolution in imaging inverse problems often degrades the generalization performance of deep networks. The authors systematically investigate the generalization behavior of U-Net and its neural operator variants across varying discretization resolutions. Through interpretable one-dimensional models and two-dimensional limited-angle computed tomography reconstruction experiments, they find that although neural operator-based U-Nets are theoretically resolution-invariant, conventional U-Nets exhibit superior robustness and practical generalization. This work highlights a notable gap between theoretical resolution invariance and empirical performance, offering new insights for architecture selection in high-resolution inverse problem solving.
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Processing through encoding: Quantum circuit approaches for point-wise multiplication and convolution
This paper addresses the low efficiency of pointwise multiplication and convolution operations in quantum signal processing with complex-valued functions. To this end, it proposes a novel “Processing through Encoding” paradigm. Methodologically, complex functions are directly encoded into auxiliary qubit states, enabling pointwise products to emerge implicitly in the final-state amplitudes; combined with Fourier-basis encoding and the inverse quantum Fourier transform (IQFT), an end-to-end quantum convolution circuit is realized. Key contributions include: (i) the first quantum pointwise multiplication scheme that requires no explicit arithmetic gates; (ii) the first integrable and verifiable quantum convolution circuit; and (iii) a theoretically complete construction, validated via numerical simulation and modular implementation using the quantumaudio toolkit—accurately generating target products and convolution outputs. This work establishes a new pathway for quantum signal processing.
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Holonomic techniques for massive 3-loop form factors: the gluonic case
研究使用高级计算机代数、自动化猜测和大规模PSLQ搜索解决了复杂胶子情况下的三圈形式因子计算问题。
0 citationsRead paper
Selection, Representation, and Execution in Sparse Fourier Neural Operators
研究通过区分稀疏表示、存储参数、理论操作数和测量运行时间，探索了稀疏Fourier神经算子的不同实现路径，以解决模型大小和推理成本问题。
0 citationsRead paper
Differentiable Jitter Correction using Deep Learning-based Image Quality Metric for Phase-Contrast Micro-CT
本文提出了一种基于深度学习图像质量度量的全可微抖动校正方法，用于X射线相衬显微CT，直接从投影数据估计和补偿每个投影的刚性抖动。
0 citationsRead paper
A neural operator view on U-Nets for inverse imaging problems
This study addresses the challenge that increasing resolution in imaging inverse problems often degrades the generalization performance of deep networks. The authors systematically investigate the generalization behavior of U-Net and its neural operator variants across varying discretization resolutions. Through interpretable one-dimensional models and two-dimensional limited-angle computed tomography reconstruction experiments, they find that although neural operator-based U-Nets are theoretically resolution-invariant, conventional U-Nets exhibit superior robustness and practical generalization. This work highlights a notable gap between theoretical resolution invariance and empirical performance, offering new insights for architecture selection in high-resolution inverse problem solving.
0 citationsRead paper
Processing through encoding: Quantum circuit approaches for point-wise multiplication and convolution
This paper addresses the low efficiency of pointwise multiplication and convolution operations in quantum signal processing with complex-valued functions. To this end, it proposes a novel “Processing through Encoding” paradigm. Methodologically, complex functions are directly encoded into auxiliary qubit states, enabling pointwise products to emerge implicitly in the final-state amplitudes; combined with Fourier-basis encoding and the inverse quantum Fourier transform (IQFT), an end-to-end quantum convolution circuit is realized. Key contributions include: (i) the first quantum pointwise multiplication scheme that requires no explicit arithmetic gates; (ii) the first integrable and verifiable quantum convolution circuit; and (iii) a theoretically complete construction, validated via numerical simulation and modular implementation using the quantumaudio toolkit—accurately generating target products and convolution outputs. This work establishes a new pathway for quantum signal processing.
0 citationsRead paper