neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However, the unique features of NA devices, such as long-range interactions, long qubit coherence time, and the ability to physically move qubits, present distinct challenges for quantum circuit compilation. In this article, we introduce DasAtom, a novel divide-and-shuttle atom approach designed to optimize Quantum circuit transformation for NA devices by leveraging these capabilities. DasAtom partitions circuits into subcircuits, each associated with a qubit mapping that allows all gates within the subcircuit to be directly executed. The algorithm then shuttles atoms to transition seamlessly from one mapping to the next, enhancing both execution efficiency and overall fidelity. For a 30-qubit Quantum Fourier Transform (QFT), DasAtom achieves a$415.8\times $improvement in fidelity over the move-based algorithm Enola and a$10.6\times $improvement over the SWAP-based algorithm Tetris. Notably, this improvement is expected to increase exponentially with the number of qubits, positioning DasAtom as a highly promising solution for scaling quantum computation on NA platforms.

This paper gives a nearly tight characterization of the quantum communication complexity of permutation-invariant Boolean functions. With such a characterization, we show that the quantum and randomized communication complexity of permutation-invariant Boolean functions are quadratically equivalent (up to a polylogarithmic factor of the input size). Our results extend a recent line of research regarding query complexity to communication complexity, showing symmetry prevents exponential quantum speedups. Furthermore, we show that the Log-rank Conjecture holds for any non-trivial total permutation-invariant Boolean function. Moreover, we establish a relationship between the quantum/classical communication complexity and the approximate rank of permutation-invariant Boolean functions. This implies the correctness of the Log-approximate-rank Conjecture for permutation-invariant Boolean functions in both randomized and quantum settings (up to a polylogarithmic factor of the input size).

Deep-learning-based approaches have achieved remarkable progress for complex real scenario denoising, yet their accuracy-efficiency tradeoff is still understudied, particularly critical for mobile devices. As real noise is unevenly distributed relative to underlay signals in different frequency bands, we introduce a frequency-aware divide-and-conquer strategy to develop a frequency-aware denoising network (FADN). FADN is materialized by stacking frequency-aware denoising blocks (FADBs), in which a denoised image is progressively predicted by a series of frequency-aware noise dividing and conquering operations. For noise dividing, FADBs decompose the noisy and clean image pairs into low- and high-frequency representations via a wavelet transform (WT) followed by an invertible network and recover the final denoised image by integrating the denoised information from different frequency bands. For noise conquering, the separated low-frequency representation of the noisy image is kept as clean as possible by the supervision of the clean counterpart, while the high-frequency representation combining the estimated residual from the successive FADB is purified under the corresponding accompanied supervision for residual compensation. Since our FADN progressively and pertinently denoises from frequency bands, the accuracy-efficiency tradeoff can be controlled as a requirement by the number of FADBs. Experimental results on the SIDD, DND, and NAM datasets show that our FADN outperforms the state-of-the-art methods by improving the peak signal-to-noise ratio (PSNR) and decreasing the model parameters. The code is released at https://github.com/NekoDaiSiki/FADN.

Soft continuum robots, characterized by their inherent compliance and dexterity, are increasingly pivotal in applications requiring delicate interactions with the environment such as the medical field. Despite their advantages, challenges persist in accurately modeling and controlling their shape during interactions with surrounding objects. This is because of the difficulty in modeling the large degrees of freedom in soft-bodied objects that become more active during interactions. In this study, we present a deep visual model to predict the interaction shapes of a soft continuum robot in contact with surrounding objects. By formulating this task as a forward-statics problem, the model uses the initial state images containing the object configuration and future actuation values to predict interactive state images of the robot under this actuation condition. We developed and tested the model in both simulated and physical environments, explored the model’s predictive capabilities using monocular and binocular views, and tested the model’s generalization ability on different datasets. Our results show that deep learning methods are a promising tool for solving the complex problem of predicting the shape of a soft continuum robot interacting with the environment, requiring no prior knowledge about the system dynamics and explicit mapping of the environment. This study paves the way for future explorations in robot-environment interaction modeling and the development of more adaptable interaction shape control strategies.

With the growing demand for video applications, many advanced learned video compression methods have been developed, outperforming traditional methods in terms of objective quality metrics such as PSNR. Existing methods primarily focus on objective quality but tend to overlook perceptual quality. Directly incorporating perceptual loss into a learned video compression framework is non-trivial and raises several perceptual quality issues that need to be addressed. In this paper, we investigated these issues in learned video compression and propose a novel High Visual-Fidelity Learned Video Compression framework (HVFVC). Specifically, we design a novel confidence-based feature reconstruction method to address the issue of poor reconstruction in newly-emerged regions, which significantly improves the visual quality of the reconstruction. Furthermore, we present a periodic compensation loss to mitigate the checkerboard artifacts related to deconvolution operation and optimization. Extensive experiments have shown that the proposed HVFVC achieves excellent perceptual quality, outperforming the latest VVC standard with only 50% required bitrate.