Accurate medical image segmentation requires effective modeling of both global anatomical structures and fine-grained boundary details. Recent state space models (e.g., Vision Mamba) offer efficient long-range dependency modeling. However, their one-dimensional serialization weakens local spatial continuity and high-frequency representation. To this end, we propose SpectralMamba-UNet, a novel frequency-disentangled framework to decouple the learning of structural and textural information in the spectral domain. Our Spectral Decomposition and Modeling (SDM) module applies discrete cosine transform to decompose low- and high-frequency features, where low frequency contributes to global contextual modeling via a frequency-domain Mamba and high frequency preserves boundary-sensitive details. To balance spectral contributions, we introduce a Spectral Channel Reweighting (SCR) mechanism to form channel-wise frequency-aware attention, and a Spectral-Guided Fusion (SGF) module to achieve adaptively multi-scale fusion in the decoder. Experiments on five public benchmarks demonstrate consistent improvements across diverse modalities and segmentation targets, validating the effectiveness and generalizability of our approach.

Quantum compilation reconciles a program's idealized interaction topology with hardware locality constraints, yet evaluations at scale lack calibrated references for realization overhead. We present QROB, a scalable reverse-construction methodology that generates compilation instances backward from directly realizable configurations, retaining the inverse paths as feasible, compiler-independent references. QROB provides a common evaluation substrate for NISQ SWAP routing and fault-tolerant lattice-surgery scheduling, while extending its reference-preserving principle to capacity-constrained quantum memory-access scheduling. Across systems ranging from 9 to 156 qubits, evaluations highlight QROB's utility as both a diagnostic benchmark and a data source. First, for compiler characterization, QROB reveals substantial realization gaps in existing tools, with NISQ compilers incurring up to 24.1x the reference SWAP cost and fault-tolerant compilers requiring up to 7.0x the reference makespan. Second, as a supervision source for data-driven compilation, a router trained on QROB references outperforms Qiskit SABRE on 84.8

The Maximum Independent Set (MIS) problem on unit-disk graphs is an NP-hard problem that can be naturally encoded on Rydberg atom systems. While existing mapping schemes enable the encoding of arbitrary graphs, current two-dimensional (2D) embedding approaches have an O(n(2)) atom overhead, limiting the size of problems solvable on near-term hardware. In this work, we present a scalable scheme that maps the MIS problem on arbitrary graphs onto programmable three-dimensional (3D) arrays of Rydberg atoms. By utilizing the extra degree of freedom provided by the third dimension, we develop a graph-reduction algorithm that embeds any graph into a 3D unit-disk graph. We show this embedding requires an overhead of O(min{m root n, n(2)}) atoms, where n and m denote the number of vertices and edges of the original graph. Furthermore, we prove that our embedding scheme is optimal for both bounded-degree graphs and dense graphs. Benchmarks on 3-regular, Erdos-Renyi, SAT-derived graphs, and standard MIS benchmark sets demonstrate that the required atom count is one to two orders of magnitude lower than state-of-the-art 2D schemes. Our work establishes a practical route toward solving large-scale, classically intractable MIS instances on near-term Rydberg quantum processors.

Quantum machine learning (QML), as an interdisciplinary field bridging quantum computing and machine learning, has garnered significant attention in recent years. Currently, the field as a whole faces challenges due to incomplete theoretical foundations for the expressivity of quantum neural networks (QNNs). In this paper we propose a constructive QNN model and demonstrate that it possesses the universal approximation property (UAP), which means it can approximate any square-integrable function up to arbitrary accuracy. Furthermore, it supports switching function bases, thus adaptable to various scenarios in numerical approximation and machine learning. Our model has asymptotic advantages over the best classical feed-forward neural networks in terms of circuit size and achieves optimal parameter complexity when approximating Sobolev functions under L_2 norm.

We consider the problem of deciding whether an n-qubit unitary (or n-bit Boolean function) is ε_1-close to some k-junta or ε_2-far from every k-junta, where k-junta unitaries act non-trivially on at most k qubits and as the identity on the rest, and k-junta Boolean functions depend on at most k variables. For constant numbers ε_1,ε_2 such that 0 < ε_1 < ε_2 < 1, we show the following. (1)A non-adaptive O(klog k)-query tolerant (ε_1,ε_2)-tester for k-junta unitaries when 2√(2)ε_1 < ε_2. (2)A non-adaptive tolerant (ε_1,ε_2)-tester for Boolean functions with O(k log k) quantum queries when 4ε_1 < ε_2. (3)A 2^O(k)-query tolerant (ε_1,ε_2)-tester for k-junta unitaries for any ε_1,ε_2. The first algorithm provides an exponential improvement over the best-known quantum algorithms. The second algorithm shows an exponential quantum advantage over any non-adaptive classical algorithm. The third tester gives the first tolerant junta unitary testing result for an arbitrary gap. Besides, we adapt the first two quantum algorithms to be implemented using only single-qubit operations, thereby enhancing experimental feasibility, with a slightly more stringent requirement for the parameter gap.

We study space-bounded communication complexity for unitary implementation in distributed quantum processors, where we restrict the number of qubits per processor to ensure practical relevance and technical non-triviality. We model distributed quantum processors using distributed quantum circuits with nonlocal two-qubit gates, defining the distributed communication complexity of a unitary as the minimum number of such nonlocal gates required for its realization, up to permutations of data qubit positions. Our contributions are twofold. First, for general n-qubit unitaries, we improve upon the trivial O(4^n) communication bound. Considering k pairwise-connected processors (each with n/k data qubits and m ancillas), we prove the communication complexity satisfies O(max{4^(1-1/k)n - m, n}) – for example, O(2^n) when m=0 and k=2 – and establish the tightness of this upper bound. We further extend the analysis to approximation models and general network topologies. Second, for special unitaries, we show that both the Quantum Fourier Transform (QFT) and Clifford circuits admit linear upper bounds on communication complexity in the exact model, outperforming the trivial quadratic bounds applicable to these cases. In the approximation model, QFT's communication complexity reduces drastically from linear to logarithmic, while Clifford circuits retain a linear lower bound. These results offer fundamental insights for optimizing communication in distributed quantum unitary implementation, advancing the feasibility of large-scale DQC systems.

In industrial wireless networks with resource-constrained and densely deployed devices, link scheduling is a challenging task. Traditional optimization methods have high computational complexity and low scalability. Graph learning offers a promising approach, yet it also comes with limitations of capturing multivariate relationships from interference, leading to ineffective link scheduling. In this article, hypergraphs are additionally introduced to model cumulative interference from concurrent transmissions. Considering the constructed comprehensive interference model, we propose a Lightweight link scheduling algorithm based on hybrid binary Graph and HyperGraph representation learning (L-GHG) in an unsupervised manner. Thereinto, the L-GHG algorithm extracts and fuses features from various interference relationships for link scheduling decisions. A graph partitioning based on channel state information is proposed to reduce redundant search space arising from cumulative interference. Simulations demonstrate that our proposed algorithm outperforms benchmark schemes regarding generalizability and scalability. We also validate the availability of the proposed algorithm in practical experiments.

Submodular maximization constitutes a prominent research topic in combinatorial optimization and theoretical computer science, with extensive applications across diverse domains. While substantial advancements have been achieved in approximation algorithms for submodular maximization, the majority of algorithms yielding high approximation guarantees are randomized. In this work, we investigate deterministic approximation algorithms for maximizing non-monotone submodular functions subject to matroid and knapsack constraints. For the two distinct constraint settings, we propose novel deterministic algorithms grounded in an extended multilinear extension framework. Under matroid constraints, our algorithm achieves an approximation ratio of (0.385 - ε), whereas for knapsack constraints, the proposed algorithm attains an approximation ratio of (0.367 -ε). Both algorithms run in poly(n) query complexity, where n is the size of the ground set, and improve upon the state-of-the-art deterministic approximation ratios of (0.367 - ε) for matroid constraints and 0.25 for knapsack constraints.

Preparing large-qubit Dicke states is of broad interest in quantum computing and quantum metrology. However, the number of qubits available on a single quantum processing unit (QPU) is limited -- motivating the distributed preparation of such states across multiple QPUs as a practical approach to scalability. In this article, we investigate the distributed preparation of $n$-qubit $k$-excitation Dicke states $D(n,k)$ across a general number $p$ of QPUs, presenting a distributed quantum circuit (each QPU hosting approximately $\lceil n/p \rceil$ qubits) that prepares the state with communication complexity $O(p \log k)$, circuit size $O(nk)$, and circuit depth $O\left(p^2 k + \log k \log (n/k)\right)$. To the best of our knowledge, this is the first construction to simultaneously achieve logarithmic communication complexity and polynomial circuit size and depth. We also establish a lower bound on the communication complexity of $p$-QPU distributed state preparation for a general target state. This lower bound is formulated in terms of the canonical polyadic rank (CP-rank) of a tensor associated with the target state. For the special case $p = 2$, we explicitly compute the CP-rank corresponding to the Dicke state $D(n,k)$ and derive a lower bound of $\lceil\log (k + 1)\rceil$, which shows that the communication complexity of our construction matches this fundamental limit.

In this work, we investigate DR-submodular maximization using stochastic biased gradients, which is a more realistic but challenging setting than stochastic unbiased gradients. We first generalize the Lyapunov framework to incorporate biased stochastic gradients, characterizing the adverse impacts of bias and noise. Leveraging this framework, we consider not only conventional constraints but also a novel constraint class: convex sets with a largest element, which naturally arises in applications such as resource allocations. For this constraint, we propose an $1/e$ approximation algorithm for non-monotone DR-submodular maximization, surpassing the hardness result $1/4$ for general convex constraints. As a direct application of stochastic biased gradients, we consider zero-order DR-submodular maximization and introduce both classical and quantum gradient estimation algorithms. In each constraint we consider, while retaining the same approximation ratio, the iteration complexity of our classical zero-order algorithms is $O(\epsilon^{-3})$, matching that of stochastic unbiased gradients; our quantum zero-order algorithms reach $O(\epsilon^{-1})$ iteration complexity, on par with classical first-order algorithms, demonstrating quantum acceleration and validated in numerical experiments.

This paper studies submodular maximization over matroids in the fully dynamic setting, where elements of an underlying ground set undergo sequential insertions and deletions. The goal is to maintain an approximate optimal solution for the current element set with a low amortized update time. For monotone submodular functions, we propose a dynamic algorithm achieving a (0.3178 - epsilon)-approximation using O-tilde(k^3) expected amortized queries, where k is the rank of the matroid constraint. Furthermore, we extend our approach to the non-monotone submodular maximization setting, obtaining a (0.1921 - epsilon)-approximation with the same update complexity. Both algorithms improve upon the best known approximation guarantees, which are (0.25 - epsilon) for the monotone case and (0.0932 - epsilon) for the non-monotone case.

We study the problem of allocating m indivisible goods among n agents, where each agent's valuation is fractionally subadditive (XOS). With respect to AnyPrice Share (APS) fairness, Kulkarni et al. (2024) showed that, when agents have binary marginal values, a 0.1222-APS allocation can be found in polynomial time, and there exists an instance where no allocation is better than 0.5-approximate APS. Very recently, Feige and Grinberg (2025) extended the problem to the asymmetric case, where agents may have different entitlements, and improved the approximation ratio to 1/6 for general XOS valuations. In this work, we focus on the asymmetric setting with binary XOS valuations, and further improve the approximation ratio to 1/2, which matches the known upper bound. We also present a polynomial-time algorithm to compute such an allocation. Beyond APS fairness, we also study the weighted maximin share (WMMS) fairness. Farhadi et al. (2019) showed that, a 1/n-WMMS allocation always exists for agents with general additive valuations, and that this approximation ratio is tight. We extend this result to general XOS valuations, where a 1/n-WMMS allocation still exists, and this approximation ratio cannot be improved even when marginal values are binary. This shows a sharp contrast to binary additive valuations, where an exact WMMS allocation exists and can be found in polynomial time.

This paper investigates convex-concave minimax optimization problems where only the function value access is allowed. We introduce a class of Hessian-aware quantum zeroth-order methods that can find the $\epsilon$-saddle point within $\tilde{\mathcal{O}}(d^{2/3}\epsilon^{-2/3})$ function value oracle calls. This represents an improvement of $d^{1/3}\epsilon^{-1/3}$ over the $\mathcal{O}(d\epsilon^{-1})$ upper bound of classical zeroth-order methods, where $d$ denotes the problem dimension. We extend these results to $\mu$-strongly-convex $\mu$-strongly-concave minimax problems using a restart strategy, and show a speedup of $d^{1/3}\mu^{-1/3}$ compared to classical zeroth-order methods. The acceleration achieved by our methods stems from the construction of efficient quantum estimators for the Hessian and the subsequent design of efficient Hessian-aware algorithms. In addition, we apply such ideas to non-convex optimization, leading to a reduction in the query complexity compared to classical methods.

Time-Critical Wireless Network (TCWN) is a promising communication technology that can satisfy the low latency, high reliability, and deterministic requirements of mission-critical applications. Multiple TCWNs required by various applications inevitably coexist with each other. Most existing works aim to achieve acceptable latency or consider the simplest topology (i.e., line topology). As latency requirements become more stringent, exploring the minimum data collection latency becomes an interesting problem. In this paper, the coexisting system consists of multiple tree-topology-based TCWNs. We first establish a conversion framework to convert an arbitrary tree topology into multiple analogous line topologies to reduce the analysis complexity. We then propose a Time-Critical wireless network Scheduling (TCS) algorithm to minimize the data collection latency of coexisting TCWNs. The TCS algorithm consists of two phases. In the internetwork scheduling phase, we strictly derive a general expression to characterize the practical network requirements. In the intranetwork scheduling phase, we design two levels of priority assignment algorithms to accurately characterize the critical states and resource requirements of different nodes. We conduct extensive simulations to verify the effectiveness of the TCS algorithm. The evaluation results show that the TCS algorithm can achieve minimum data collection latency in more than 99.956% cases, and the maximum difference compared to the optimal value is one time slot.

Industrial Control Systems (ICSs) are the core of industrial production. Wireless technology, with its flexibility and adaptability, is catalyzing a transformative shift from traditional ICS to the advanced Industrial Wireless Control Systems (IWCSs). However, the openness of wireless media, high dynamics of the environment, and resource scarcity present unprecedented security challenges of high security defense costs and low detection inaccuracy for IWCS. State-of-the-art methods primarily treat ICS as a typical cyber-physical system, which focuses on security issues from the cyber and control domains, rather than the physical domain. As a result, they are unable to fully address the high dynamics of wireless channels and unknown attacks, ultimately failing to meet the stringent security requirements of industrial systems. To this end, this paper proposes a physical-domain whitelist as the final line of security defense leveraging the finite nature of the physical behavior space in industrial production systems. Moreover, a holistic cross-domain security-safety architecture is introduced, drawing inspiration from the integrated cyber-control-physical collaboration. In the proposed architecture, the top-down inherent security-safety defense and bottom-up risk backtracking form a close loop, which not only prevents unknown attacks but also facilitates rapid localization and response to attacks. In the experiment, the composite AGV scheduling control has been developed to verify the effectiveness of the architecture. Ultimately, the potential challenges of the cross-domain architecture for IWCS safety-security defense have been summarized.

Symmetric submodular maximization is an important class of combinatorial optimization problems, including MAX-CUT on graphs and hyper-graphs. The state-of-the-art algorithm for the problem over general constraints has an approximation ratio of 0.432 [16]. The algorithm applies the canonical continuous greedy technique that involves a sampling process. It, therefore, suffers from high query complexity and is inherently randomized. In this paper, we present several efficient deterministic algorithms for maximizing a symmetric submodular function under various constraints. Specifically, for the cardinality constraint, we design a deterministic algorithm that attains a 0.432 ratio and uses O(kn) queries. Previously, the best deterministic algorithm attains ( a 0.385-e ratio and uses O kn(10 9e ) 20 9e-1) queries [12]. For the matroid constraint, we design a deterministic algorithm that attains a 1/3-e ratio and uses O(kn log e-1) queries. Previously, the best deterministic algorithm can also attain 1/3-e ratio but it uses much larger O(e-1n4) queries [24]. For the packing constraints with a large width, we design a deterministic algorithm that attains a 0.432-e ratio and uses O(n2) queries. To the best of our knowledge, there is no deterministic algorithm for the constraint previously. The last algorithm can be adapted to attain a 0.432 ratio for single knapsack constraint using O(n4) queries. Previously, the best deterministic algorithm attains a 0.316-e ratio and uses O(n3) queries [2].

Trichoderma longibrachiatum SMF2 is an important biocontrol fungus that can control pathogen through a variety of mechanisms, such as competition, antibiosis, and induction of plant disease resistance. However, the effect of proteins secreted by this microorganism on plants is not well understood. Therefore, we investigated the function of the secreted protein TLCpe1(Cpe1), which is homologous to cerato-platanin-like effector, by constructing gene knockout mutants and analyzing their phenotypes. After transforming the knockout vector of gene TLCPE1 (CPE1) into the protoplast of T. longibrachiatum SMF2, a mutant LCPE1 was identified. The results of PCR and southern blotting confirmed that the vector was inserted into the genome by single exchange of the upstream recombination arm, resulting in the disruption of integrity gene. RT-PCR demonstrated that CPE1 gene was not normally transcribed in the LCPE1 mutant. Phenotypic analysis revealed that the growth rate and spore yield were reduced in LCPE1. However, LCPE1 showed no significant difference in inhibitory activity against B. cinerea compared to the wild type of T. longibrachiatum SMF2 (WT). Root inoculation assays showed that when T. repens were treated with a spore suspension of the mutant LCPE1, the induced resistance of plant to B. cinerea was reduced compared to that treated with the WT. Further analysis indicated that T. repens treated with a WT spore suspension exhibited significantly higher levels of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activity than plants treated with LCPE1, while the content of malondialdehyde (MDA) in T. repens treated with the WT was relatively low. These findings indicate that the secreted protein Cpe1 functions as an effector that enhances host resistance during the biocontrol process of T. longibrachiatum SMF2.

The Quantum Approximate Optimization Algorithm (QAOA) is widely studied for combinatorial optimization and has achieved significant advances both in theoretical guarantees and practical performance, yet for general combinatorial optimization problems the expected performance and classical simulability of fixed-round QAOA remain unclear. Focusing on Max-Cut, we first show that for general graphs and any fixed round p≥2, exactly evaluating the expectation of fixed-round QAOA at prescribed angles is NP-hard, and that approximating this expectation within additive error 2^-O(n) in the number n of vertices is already NP-hard. To evaluate the expected performance of QAOA, we propose a dynamic programming algorithm leveraging tree decomposition. As a byproduct, when the p-local treewidth grows at most logarithmically with the number of vertices, this yields a polynomial-time exact evaluation algorithm in the graph size n. Beyond Max-Cut, we extend the framework to general Binary Unconstrained Combinatorial Optimization (BUCO). Finally, we provide reproducible evaluations for rounds up to p=3 on representative structured families, including the generalized Petersen graph GP(15,2), double-layer triangular 2-lifts, and the truncated icosahedron graph C_60, and report cut ratios while benchmarking against locality-matched classical baselines.

Camouflaged Object Detection (COD) aims to segment camouflaged objects hidden within their environment. Existing COD models, aside from image features, mostly focus on a single coarse-grained spatial structure, such as depth information, texture information, or edge information. However, when faced with complex scenes where the target and background textures are similar and overlapping, or when subjected to noise interference, this design often leads to insufficient detection accuracy and robustness. To address these issues, we proposed a strategy for multiple spatial explorations and designed Spatial Bi-Exploration Network (SPNet). SPNet conducts a comprehensive analysis of complex camouflage scenarios by jointly exploring depth spatial, contour spatial, and image feature information, thereby enhancing detection performance and maintaining robustness. Unlike existing methods, SPNet leverages dual exploration of depth and contour spaces to mitigate the vulnerability of coarse structures to noise. Depth spatial information aids the model in recognizing the deep relationships between objects and the background, reducing the impact of noise on object boundaries, while contour spatial information improves edge detection accuracy. This dual approach significantly enhances robustness, especially in the face of adversarial attacks. Extensive experiments on benchmark datasets demonstrate that our model not only outperforms existing methods in detection performance but also exhibits superior robustness against adversarial attacks.