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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Quantum Brush: A quantum computing-based tool for digital painting
This work addresses the absence of quantum-native interaction paradigms in digital art creation by proposing the first quantum painting framework tailored for Noisy Intermediate-Scale Quantum (NISQ) devices. Methodologically, it introduces four types of quantum brushes that encode user strokes in real time into parameterized quantum circuits; quantum superposition and entanglement are leveraged to generate visual textures provably infeasible to simulate classically. The framework integrates quantum state encoding, measurement-induced wavefunction collapse, and classical rendering interfaces to enable hardware-level real-time interaction. Contributions include: (1) the first end-to-end quantum painting deployment on real quantum hardware (IQM Sirius); (2) an open-source toolchain empirically validated for robust artistic output on noisy, intermediate-scale devices; and (3) a demonstration that quantum phenomena—such as interference and quantum correlations—can be systematically harnessed as novel aesthetic primitives, thereby expanding the frontier of quantum computing applications in creative AI.
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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。
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
Quantum Brush: A quantum computing-based tool for digital painting
This work addresses the absence of quantum-native interaction paradigms in digital art creation by proposing the first quantum painting framework tailored for Noisy Intermediate-Scale Quantum (NISQ) devices. Methodologically, it introduces four types of quantum brushes that encode user strokes in real time into parameterized quantum circuits; quantum superposition and entanglement are leveraged to generate visual textures provably infeasible to simulate classically. The framework integrates quantum state encoding, measurement-induced wavefunction collapse, and classical rendering interfaces to enable hardware-level real-time interaction. Contributions include: (1) the first end-to-end quantum painting deployment on real quantum hardware (IQM Sirius); (2) an open-source toolchain empirically validated for robust artistic output on noisy, intermediate-scale devices; and (3) a demonstration that quantum phenomena—such as interference and quantum correlations—can be systematically harnessed as novel aesthetic primitives, thereby expanding the frontier of quantum computing applications in creative AI.
0 citationsRead paper