Simulating correlated materials on present-day quantum hardware remains challenging due to limited quantum resources. Quantum embedding methods offer a promising route by reducing computational complexity through the mapping of bulk systems onto effective impurity models, allowing more feasible simulations on pre- and early-fault-tolerant quantum devices. This work develops a quantum-classical embedding framework based on the ghost Gutzwiller approximation to enable quantum-enhanced simulations of ground-state properties and spectral functions of correlated electron systems. Circuit complexity is analyzed using an adaptive variational quantum algorithm on a statevector simulator, applied to the infinite-dimensional Hubbard model with increasing ghost mode numbers from 3 to 5, resulting in circuit depths growing from 16 to 104. Noise effects are examined using a realistic error model, revealing significant impact on the spectral weight of the Hubbard bands. To mitigate these effects, the Iceberg quantum error detection code is employed, achieving up to 40% error reduction in simulations. Finally, the accuracy of the density matrix estimation is benchmarked on IBM and Quantinuum quantum hardware, featuring distinct qubit-connectivity and employing multiple levels of error mitigation techniques.

In this work, we introduce maximally recoverable codes with locality and availability. We consider locally repairable codes (LRCs) where certain subsets of $ t $ symbols belong each to $ N $ local repair sets, which are pairwise disjoint after removing the $ t $ symbols, and which are of size $ r+δ-1 $ and can correct $ δ-1 $ erasures locally. Classical LRCs with $ N $ disjoint repair sets and LRCs with $ N $-availability are recovered when setting $ t = 1 $ and $ t=δ-1=1 $, respectively. Allowing $ t > 1 $ enables our codes to reduce the storage overhead for the same locality and availability. In this setting, we define maximally recoverable LRCs (MR-LRCs) as those that can correct any globally correctable erasure pattern given the locality and availability constraints. We then identify a large class of global erasure patterns that can be corrected by such MR-LRCs and prove that they are all the correctable patterns when $ t=1 $. We provide three explicit constructions of LRCs that can correct such erasure patterns (thus MR-LRCs for $ t=1 $), based on MSRD codes, each attaining the smallest finite-field sizes for some parameter regime. Finally, we extend the known lower bound on finite-field sizes from classical MR-LRCs to our setting (for any value of $ t $).

In this paper, we extend Feigin-Frenkel duality at the critical level to complex rank by identifying two seemingly unrelated constructions in complex rank. On the affine side, we interpolate Molev's construction of higher Segal-Sugawara vectors and thereby describe the centers of universal affine vertex algebras at the critical level in Deligne's interpolating categories. On the $\mathcal{W}$-side, we construct the classical $\mathcal{W}$-algebras associated with Feigin's Lie algebras of complex rank $\mathfrak{gl}_λ$ and $\mathfrak{po}_λ$ as Poisson vertex algebras, realizing their Drinfeld-Sokolov reduction via an interpolated Adler-Gelfand-Dickey bracket. Upon specialization to positive integer rank in types A, B, and C, this recovers the usual Feigin-Frenkel duality at the critical level. As applications, we obtain a uniform construction of several families of higher Segal-Sugawara vectors for Lie superalgebras and recover a complex-rank analogue of the universal Bethe algebra.

Simulating Clifford and near-Clifford circuits using the extended stabilizer formalism has become increasingly popular, particularly in quantum error correction. Compared to the state-vector approach, the extended stabilizer formalism can solve the same problems with fewer computational resources, as it operates on stabilizers rather than full state vectors. Most existing studies on near-Clifford circuits focus on balancing the trade-off between the number of ancilla qubits and simulation accuracy, often overlooking performance considerations. Furthermore, in the presence of high-rank stabilizers, performance is limited by the sequential property of the stabilizer formalism. In this work, we introduce a parallelized version of the extended stabilizer formalism, enabling efficient execution on multi-core devices such as GPU. Experimental results demonstrate that, in certain scenarios, our Python-based implementation outperforms state-of-the-art simulators such as Qiskit and Pennylane.

Data fusion models are widely used in air quality monitoring to integrate in situ and large-scale gridded products, offering spatially complete and temporally detailed estimates. However, traditional Gaussian-based models often underestimate extreme pollution values, leading to biased risk assessments. To address this, we present a Bayesian hierarchical data fusion framework rooted in extreme value theory, using the Dirac-delta generalised Pareto distribution to jointly account for threshold and non-threshold exceedances while preserving the timing of exceedance and non-exceedance episodes. Our model is used to describe and predict censored threshold exceedances of PM2.5 pollution in the Greater London region by using CAMS atmospheric composition reanalysis, and in situ observation stations from the automatic urban and rural network (AURN) run by the UK government. Key features of our approach include combining data with varying spatio-temporal resolutions and fully accounting for parameter uncertainties. Results show that our model outperforms Gaussian-based alternatives and standalone reanalysis data in predicting threshold exceedances at the majority of observation sites and can even result in improved spatial patterns of PM2.5 pollution than those discernible from the background data. Moreover, our approach captures greater variability and spatial patterns, such as higher PM2.5 concentrations near coastal areas, which are not evident in the reanalysis data alone.

Psychological safety refers to the belief that team members can speak up or make mistakes without fear of negative consequences. While it is recognized as important in traditional software teams, its role in open-source software development remains understudied. Open-source contributors often collaborate without formal roles or structures, where interpersonal relationships can significantly influence participation. Code review, a central and collaborative activity in modern software development, offers a valuable context for observing such team interactions. This paper introduces a framework grounded in psychological safety theory to identify behaviors that signal PS in pull request interactions. We operationalize these behaviors using 10 observable variables derived from 60,684 PRs across 26 popular GitHub repositories and construct a PS index at repository level. We then empirically test the relationship between this index and contributors' short-term (within 1 year) and long-term (over 4-5 years) sustained participation using three logistic regression models. Contributors are more likely to remain active in repositories with higher levels of psychological safety. Psychological safety is positively associated with both short-term and long-term sustained participation. However, prior participation emerges as a stronger predictor of future engagement, reducing the effect of psychological safety when accounted for. This study introduces a a theory-informed framework for measuring psychological safety through pull request data and provides empirical evidence of its relevance in sustaining participation within open-source development.

Artificial Intelligence (AI) holds transformative potential in education, enabling personalized learning, enhancing inclusivity, and encouraging creativity and curiosity. In this paper, we explore how Large Language Models (LLMs) can act as both patient tutors and collaborative partners to enhance education delivery. As tutors, LLMs personalize learning by offering step-by-step explanations and addressing individual needs, making education more inclusive for students with diverse backgrounds or abilities. As collaborators, they expand students' horizons, supporting them in tackling complex, real-world problems and co-creating innovative projects. However, to fully realize these benefits, LLMs must be leveraged not as tools for providing direct solutions but rather to guide students in developing resolving strategies and finding learning paths together. Therefore, a strong emphasis should be placed on educating students and teachers on the successful use of LLMs to ensure their effective integration into classrooms. Through practical examples and real-world case studies, this paper illustrates how LLMs can make education more inclusive and engaging while empowering students to reach their full potential.

Machine learning (ML) has shown promise in vulnerability detection, but ML detectors may rely on irrelevant code features, causing them to highlight non-vulnerable lines as suspicious. Such misleading predictions increase developers' manual effort and may lead to incorrect patching strategies, motivating the need to identify untrustworthy predictions automatically. We present UntrustVul, an approach for detecting untrustworthy vulnerability predictions by identifying suspicious lines that are inherently unrelated to vulnerabilities. UntrustVul leverages patterns from historical vulnerable lines and flags predictions as untrustworthy when the highlighted lines neither match known vulnerability patterns nor influence lines that do. A line is considered vulnerability-irrelevant if it does not resemble historical vulnerabilities and all its successors in the data and control dependency graph are also vulnerability-irrelevant. The approach is designed conservatively to minimise misclassifying trustworthy predictions as untrustworthy. We evaluate UntrustVul on 115K predictions from four models across the BigVul, MegaVul, SARD, and PrimeVul datasets. Results show that UntrustVul achieves AUC scores of 70%-88% and F1-scores of 82%-94%, outperforming existing approaches by 6%-59% in AUC and 13%-92% in F1-score.

Observing superdiffusive scaling in the spin transport of the integrable 1D Heisenberg model is one of the key discoveries in non-equilibrium quantum many-body physics. Despite this remarkable theoretical development and the subsequent experimental observation of the phenomena in KCuF$_3$, real materials are often imperfect and contain integrability breaking interactions. Understanding the effect of such terms on the superdiffusion is crucial in identifying connections to such materials. Current quantum hardware has already ascertained its utility in studying such non-equilibrium phenomena by simulating the superdiffusion of the 1D Heisenberg model. In this work, we perform a quantum simulation of the superdiffusion breakdown by generalizing the superdiffusive Floquet-type 1D Heisenberg model to a general 2D model. We comprehensively study the effect of different 2D interactions on the superdiffusion breakdown by tuning up their strength from zero, corresponding to the 1D Heisenberg chain, to finite nonzero values. We observe that certain 2D interactions are more resilient against superdiffusion breakdown than others and that the $SU(2)$ preserving 2D interaction has the highest resilience among all the 2D interactions we study. Importantly, this observed resilience has direct implications for sustaining superdiffusive spin transport in two-dimensional lattices. We reason out the relative resilience against the superdiffusion breakdown through an analysis of the scattering coefficients off the 2D interaction in otherwise 1D chains. The relative resilience of different interaction types against superdiffusion breakdown was also captured in quantum hardware with remarkable accuracy, further establishing the current quantum hardware's applicability in simulating interesting non-equilibrium quantum many-body phenomena.

Distributional regression aims to find the best candidate in a given parametric family of conditional distributions to model a given dataset. As each candidate in the distribution family can be identified by the corresponding distribution parameters, a common approach for this task is to use the maximum likelihood estimator (MLE) for the parameters. In this paper, we establish theoretical results for this estimator in case the response variable is subject to random right censoring. In particular, we provide proofs of almost sure consistency and asymptotic normality of the MLE under censoring. The empirical behavior is illustrated by a simulation study and a real data example.

In this note, we show that the $p$-adic periods of motives introduced recently by Ancona and Frăţilă (``André periods'') reduce to the classically studied notion in the case of Mixed Tate motives. We also connect André periods with Coleman integration by observing that the Frobenius-fixed de Rham paths of Besser and Vologodsky come from motivic paths in characteristic $p$ (unconditionally in the mixed Tate setting, conditionally in general). We use this to realize special values of $p$-adic multiple polylogarithms as André periods in a concrete way.

Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions. This paper presents a comprehensive framework to address these challenges through three key contributions. First, we systematically review global AI governance laws and policies from governments and regulatory bodies, as well as industry practices and standards. Based on this analysis, we propose a set of guiding principles for GenFMs, developed through extensive multidisciplinary collaboration that integrates technical, ethical, legal, and societal perspectives. Second, we introduce TrustGen, the first dynamic benchmarking platform designed to evaluate trustworthiness across multiple dimensions and model types, including text-to-image, large language, and vision-language models. TrustGen leverages modular components--metadata curation, test case generation, and contextual variation--to enable adaptive and iterative assessments, overcoming the limitations of static evaluation methods. Using TrustGen, we reveal significant progress in trustworthiness while identifying persistent challenges. Finally, we provide an in-depth discussion of the challenges and future directions for trustworthy GenFMs, which reveals the complex, evolving nature of trustworthiness, highlighting the nuanced trade-offs between utility and trustworthiness, and consideration for various downstream applications, identifying persistent challenges and providing a strategic roadmap for future research. This work establishes a holistic framework for advancing trustworthiness in GenAI, paving the way for safer and more responsible integration of GenFMs into critical applications. To facilitate advancement in the community, we release the toolkit for dynamic evaluation.

The process tensor provides a general representation of a quantum system evolving under repeated interventions and is fundamental for numerical simulations of local many-body dynamics. In this work, we introduce the projected process ensemble, an ensemble of pure output states of a process tensor in a given basis of local interventions, and use it to define increasingly more fine-grained probes of quantum chaos. The first moment of this ensemble encapsulates numerous previously studied chaos quantifiers, including the Alicki-Fannes quantum dynamical entropy, butterfly flutter fidelity, and spatiotemporal entanglement. We discover characteristic entanglement structures within the ensemble's higher moments that can sharply distinguish chaotic from integrable dynamics, overcoming deficiencies of the quantum dynamical and spatiotemporal entropies. These conclusions are supported by extensive numerical simulations of many-body dynamics for a range of spin-chain models, including non-interacting, interacting-integrable, chaotic, and many-body localized regimes. Our work elucidates the fingerprints of chaos on spatiotemporal correlations in quantum stochastic processes, and provides a unified framework for analyzing the complexity of unitary and monitored many-body dynamics.

In this paper, we propose a general approach for approximate simulation and analysis of delay differential equations (DDEs) with distributed time delays based on methods for ordinary differential equations (ODEs). The key innovation is that we 1) propose an Erlang mixture approximation of the kernel in the DDEs and 2) use the linear chain trick to transform the resulting approximate DDEs to ODEs. Furthermore, we prove that the approximation converges for continuous and bounded kernels and for specific choices of the coefficients if the number of terms increases sufficiently fast. We show that the approximate ODEs can be used to assess the stability of the steady states of the original DDEs and that the solution to the ODEs converges if the kernel is also exponentially bounded. Additionally, we propose an approach based on bisection and least-squares estimation for determining optimal parameter values in the approximation. Finally, we present numerical examples that demonstrate the accuracy and convergence rate obtained with the optimal parameters and the efficacy of the proposed approach for bifurcation analysis and Monte Carlo simulation. The numerical examples involve a modified logistic equation, chemotherapy-induced myelosuppression, and a point reactor kinetics model of a molten salt nuclear fission reactor.

The Landau-Lifshitz equation describes the dynamics of magnetization in ferromagnetic materials. Due to the essential nonlinearity and nonconvex constraint, it is typically solved numerically. In this paper, we developed a finite volume element method (FVEM) with the Gauss-Seidel projection method (GSPM) for the micromagnetics simulations. We provide the approximation error in space and depict the energy law when the FVEM is adopted. Owing to the GSPM for time-marching, the discrete system is decoupled component by component, making the computational complexity comparable to that of solving the scalar heat equation implicitly. This significantly accelerates real simulations. We present several numerical experiments to validate the theoretical analysis and the efficiency gain. Additionally, we study the blow-up solution and efficiently simulate the 2D magnetic textures using the proposed method.

We show that the higher-order Weyl algebras over a field of characteristic zero, which are formally rigid as associative algebras, can be formally deformed in a nontrivial way as hom-associative algebras. We also show that these hom-associative Weyl algebras arise naturally as hom-associative iterated differential polynomial rings, that they contain no zero divisors, are power-associative only when associative, and that they are simple. We then determine their commuters, nuclei, centers, and derivations. Last, we classify all hom-associative Weyl algebras up to isomorphism and conjecture that all nonzero homomorphisms between any two isomorphic hom-associative Weyl algebras are isomorphisms. The latter conjecture turns out to be stably equivalent to the Dixmier Conjecture, and hence also to the Jacobian Conjecture.

The renowned Local Friendliness no-go theorem demonstrates the incompatibility of quantum theory with the combined assumptions of Absoluteness of Observed Events - the idea that observed outcomes are singular and objective - and Local Agency - the requirement that the only events correlated with a setting choice are in its future light cone. Granted that the Local Friendliness scenario can be realized, this result is stronger than Bell's theorem because the assumptions of Local Friendliness are weaker than those of Bell's theorem: Local Agency is less restrictive than local causality, and Absoluteness of Observed Events is encompassed within the notion of realism assumed in Bell's theorem. Drawing inspiration from the correspondence between nonlocality proofs in Bell scenarios and generalized contextuality proofs in prepare-and-measure scenarios, we present the Noncontextual Friendliness no-go theorem. This theorem demonstrates the inconsistency of quantum theory with the joint assumptions of Absoluteness of Observed Events and Noncontextual Agency, the latter being a weaker version of noncontextuality, in the same way that Local Agency is a weaker version of local causality. Our result generalizes the Local Friendliness no-go theorem and, granted that the scenario can be realized, is stronger than no-go theorems based on generalized noncontextuality.

Tensor network formalisms have emerged as powerful tools for simulating quantum state evolution. While widely applied in the study of optical quantum circuits, such as Boson Sampling, existing tensor network approaches fail to address the complexity mismatch between tensor contractions and the calculation of photon-counting probability amplitudes. Here, we present an alternative tensor network framework that exploits the input-output relations of quantum optical circuits encoded in the unitary interferometer matrix. Our approach bridges the complexity gap by enabling the computation of the permanent -- central to Boson Sampling -- with the same computational complexity as the best known classical algorithm based on a graphical representation of the operator-basis MPS that we introduce. Furthermore, we exploit the flexibility of tensor networks to extend our formalism to incorporate partial distinguishability and photon loss, two key imperfections in practical interferometry experiments. This work offers a significant step forward in the simulation of large-scale quantum optical systems and the understanding of their computational complexity.

In this work we present a novel linear and positivity preserving upwind discontinuous Galerkin (DG) approximation of a class of chemotaxis models with damping gradient nonlinearities. In particular, both a local and a nonlocal model including nonlinear diffusion, chemoattraction, chemorepulsion and logistic growth are considered. Some numerical experiments in the context of chemotactic collapse are presented, whose results are in accordance with the previous analysis of the approximation and show how the blow-up can be prevented by means of the damping gradient term.

We investigate the large deviation principle (LDP) of the stationary solutions of stochastic functional differential equations (SFDEs) with infinite delay under small random perturbation. First, we demonstrate the existence and uniqueness of the corresponding stationary solutions. Second, by the weak convergence approach, we show the uniform large deviation principle for the solution maps, and then prove the LDP for stationary solutions. Furthermore, we obtain the LDP for invariant measures of SFDEs through the LDP for stationary solutions and the contraction principle.

The Syncytial Mesh Model introduces a three-layered framework for large-scale brain dynamics integrating local neural circuitry, macrostructural connectivity, and a slow mesoscale control-field substrate associated with astrocytic syncytial organization. Rather than directly generating electrophysiological activity, the proposed syncytial layer modulates neuronal excitability, coherence structure, and metastable coordination across spatial scales. The framework is formulated as a phenomenological effective theory combining neural-mass dynamics, connectome-scale coupling, and continuous-field interactions. Within this architecture, the model provides a candidate explanation for large-scale traveling-wave organization, low-frequency coherence structure, and distributed plasticity phenomena that are not straightforwardly reducible to direct local synaptic connectivity alone. Numerical simulations of the effective field dynamics generate stable traveling-wave propagation, smooth phase-gradient organization, and low-frequency modal structure qualitatively resembling experimentally reported infra-slow and delta/theta coordination patterns. An analytic mesoscale coherence model further illustrates how scale-dependent synchronization probabilities may emerge from slow-field modulation and damping dynamics without requiring globally phase-locked neuronal oscillations.

Many open source software (OSS) projects need more human resources for maintenance, improvements, and sometimes even their survival. These needs allegedly apply even to vital OSS projects that can be seen as being a part of the world's critical infrastructures. To address this resourcing problem, new funding instruments for OSS projects have been established in recent years. The paper examines two such funding bodies for OSS and the projects they have funded. The focus of both funding bodies is on software security and cyber security in general. Based on qualitative thematic analysis, the results indicate that particularly OSS supply chains, network and cryptography libraries, programming languages, and operating systems and their low-level components have been funded and thus seen as critical in terms of cyber security. In addition to the qualitative results presented, the paper makes a contribution by connecting the research branches of critical infrastructure and sustainability of OSS projects. A further contribution is made by connecting the topic examined to recent cyber security regulations. Finally, an important argument is raised that neither cyber security nor project sustainability alone can entirely explain the rationales behind the funding decisions made by the two funding bodies.

The abelian $(p+1)$-form gauge field is inherently coupled to the $p$-brane worldvolume. After quantization, the corresponding $p$-form gauge transformation is associated with the local phase ambiguity of the $p$-brane wave functional. In essence, the $p$-form gauge symmetry can be realized as a special construction of the generic 0-form gauge symmetry in the functional space of $p$-brane configurations. The non-abelian generalization is straightforward in the functional space language. To simplify the analysis, we further introduce a toy model where the infinite dimensional functional space of $p$-brane configurations is replaced by a finite dimensional matrix space. After taking the symmetric trace in the matrix model, the original discussions of the $p$-form gauge symmetry can be inherited by the toy model.

We compare a couple of notions of differential form on singular complex algebraic varieties, and relate them to the outermost associated graded spaces of the Hodge filtration of ordinary and intersection cohomology. In particular, we introduce and study singularities, that we call quasi-rational, which are normal and such that for all p, the zeroth cohomology sheaf of the complex of Du Bois p-forms is isomorphic to the direct image of p-forms from a desingularization. We show that an isolated singularity is rational if and only if it is quasi-rational, Du Bois, and certain Hodge numbers of the local mixed Hodge structures vanish.

Our microscopic understanding of the integer quantum Hall effect is still incomplete. For decades, there has been a controversial discussion about "where the current flows" if the Hall resistance is quantized. Here, we qualitatively analyze the current density distribution in a Hall bar based on the screening properties of a two-dimensional electron system in the quantum Hall regime. Beyond previous publications, we include a closed loop persistent current that exists inside a Hall bar if the Hall resistance is quantized. We find, that the persistent current density decreases with increasing Hall voltage. Accounting for this dependence, we find, that the current flows in the opposite directions along opposite edges of the Hall bar, while the imposed current flows unidirectionally and only on the side of the Hall bar connected with its higher electrical potential edge.

Recently, generative semantic communication models have proliferated as they are revolutionizing semantic communication frameworks, improving their performance, and opening the way to novel applications. Despite their impressive ability to regenerate content from the compressed semantic information received, generative models pose crucial challenges for communication systems in terms of high memory footprints and heavy computational load. In this paper, we present a novel Quantized GEnerative Semantic COmmunication framework, Q-GESCO. The core method of Q-GESCO is a quantized semantic diffusion model capable of regenerating transmitted images from the received semantic maps while simultaneously reducing computational load and memory footprint thanks to the proposed post-training quantization technique. Q-GESCO is robust to different channel noises and obtains comparable performance to the full precision counterpart in different scenarios saving up to 75% memory and 79% floating point operations. This allows resource-constrained devices to exploit the generative capabilities of Q-GESCO, widening the range of applications and systems for generative semantic communication frameworks. The code is available at https://github.com/ispamm/Q-GESCO.

We prove that over an algebraically closed field of characteristic $p>0$ there are exactly, up to isomorphism, $n$ infinitesimal commutative unipotent $k$-group schemes of order $p^n$ with one-dimensional Lie algebra, and we explicitly describe them. We consequently obtain an explicit description of all infinitesimal subgroup schemes of any supersingular elliptic curve over an algebraically closed field, recovering all their $p^n$-torsions as well. Finally, we use these results to answer a question of Brion on rational actions of infinitesimal commutative unipotent group schemes on curves.

The concept of $S$-characters of finite groups was introduced by Zhmud' as a generalisation of transitive permutation characters. Any non-trivial $S$-character takes a zero value on some group element. By a deep result depending on the classification of finite simple groups a non-trivial transitive permutation character even vanishes on some element of prime power order. We present examples that this does not generalise to $S$-characters, thereby answering a question posed by J-P. Serre.

We present a formula for trigonometric orthosymplectic $R$-matrices associated with any parity sequence, and establish their factorization into the ordered product of $q$-exponents parametrized by positive roots in the corresponding reduced root systems. The latter is crucially based on the construction of orthogonal bases of the positive subalgebra through $q$-bracketings and combinatorics of dominant Lyndon words, as developed in [Clark, Hill, Wang, "Quantum shuffles and quantum supergroups of basic type", Quantum Topol. 7 (2016), no.3, 553-638]. We further evaluate the affine orthosymplectic $R$-matrices, establishing their intertwining property as well as matching them with those obtained through the Yang-Baxterization technique of [Ge, Wu, Xue, "Explicit trigonometric Yang-Baxterization", Internat. J. Modern Phys. A 6 (1991), no.21, 3735-3779]. This reproduces the celebrated formulas of [Jimbo, "Quantum $R$ matrix for the generalized Toda system", Comm. Math. Phys. 102 (1986), no.4, 537-547] for classical BCD types and the formula of [Mehta, Dancer, Gould, Links, "Generalized Perk-Schultz models: solutions of the Yang-Baxter equation associated with quantized orthosymplectic superalgebras", J. Phys. A 39 (2006), no.1, 17-26] for the standard parity sequence.

We consider the depolarizing channel in $d$ dimension defined as $D_x(ρ)=(1-x)ρ+x\: \textit{tr}(ρ) \frac{I}{d}$, and explicitly find a quantum channel ${\cal N}_x$ which anti-degrades this, when $x\geq\frac{1}{2}$. This proves that the depolarizing channel $D_x$ has zero capacity when $x\geq\frac{1}{2}$. As a corollary, this implies that any quantum channel when contaminated by white noise stronger than this value loses its capacity completely. Although by arguments based on symmetric-extendibiliy of the Choi matrix, it is known that the channel is anti-degradable when $x\geq \frac{d}{2(d+1)}$, the explicit form of the anti-degrading channel in this larger interval is not known. We also calculate in closed form the capacity of the complenetary channel ${\cal D}_x^c$ in the region $x\geq \frac{1}{2}$. This adds to the existing list of quantum channels for which the quantum capacity has been calculated in closed form.