All categories
DevOps & Cloud
Deployment, cloud, containers, CI/CD, monitoring, and infrastructure
- 20,289
- 846
Security grades appear on each card once the skill has been scanned. Newly imported skills may briefly show without a grade until the backfill job runs.
Open in full browserBrowse devops & cloud skills
Showing 7,129–7,152 of 20,289 skills
- Quantum Network Routing OptimizationQuantum-Inspired Hamiltonian Optimization for large-scale QKD network routing methodology. Combines stochastic tensor networks with adaptive congestion routing for optimizing latency, secret key rate, and security in quantum networks. Use when: (1) designing QKD network routing, (2) optimizing quantum network traffic, (3) quantum key distribution infrastructure, (4) adaptive network congestion management, (5) quantum communication system design.Votes: 0GitHub stars: 3
- Quantum Ml HealthcareResearch and application patterns for quantum machine learning in healthcare. Covers QNNs for medical imaging, hybrid quantum-classical models for diagnosis, and quantum advantage in biomedical data analysis. Use when researching or implementing quantum computing applications in medical diagnosis, healthcare AI, quantum neural networks for biomedical imaging, or hybrid quantum-classical healthcare systems. Trigger: quantum healthcare, quantum medical, QNN diagnosis, quantum ML medicine, quant...Votes: 0GitHub stars: 3
- Quantum Medical Feature FusionAdaptive hybrid quantum-classical feature fusion for medical image classification. Combines classical deep learning backbones (ResNet, ViT) with parameterized quantum circuits via three progressive fusion strategies: Static Hybrid Fusion (SHF), Dynamic Hybrid Fusion (DHF), and Temperature-Scaled Hybrid Fusion (TSHF). TSHF uses a learnable scalar to dynamically balance hybrid gradient dynamics and resolve optimization asymmetry, achieving 87.82% accuracy on BreastMNIST. Use when building hybri...Votes: 0GitHub stars: 3
- Quantum Medical DiagnosisFramework for applying quantum machine learning to medical diagnosis tasks. Covers QNN architectures, encoding strategies, and evaluation methodologies for clinical data. Trigger: quantum medical diagnosis, QNN healthcare, quantum clinical prediction, medical quantum MLVotes: 0GitHub stars: 3
- Quantum Global Variational Learning QecQuantum Global Variational Learning for Quantum Error Correction methodology — quantum neural network with global structure reducing unitary matrices in QEC circuits, achieving 97% training time reduction, 25% completion rate improvement, and 15% fidelity increase under noise. Applicable to quantum error correction, quantum neural network design, variational quantum algorithms, training efficiency optimization.Votes: 0GitHub stars: 3
- Quantum Federated Healthcare CommunicationCommunication-efficient Quantum Federated Learning (QFL) methodology for privacy-sensitive healthcare. Introduces Hybrid QFL architecture with light-cone feature selection and dynamic centralized/decentralized aggregation switching. Use when designing quantum-secure distributed learning systems for medical data.Votes: 0GitHub stars: 3
- Quantum Enhanced Coronary ClassificationLightweight quantum-enhanced ResNet for coronary angiography (CAG) classification. Combines classical CNN backbones with variational quantum circuits for medical image classification. Use when: coronary angiography analysis, cardiac image classification, lightweight QML models, quantum-enhanced CNNs, operator-dependency reduction in CAG interpretation, or hybrid quantum-classical medical imaging.Votes: 0GitHub stars: 3
- Quantum Access Network QkdPassive quantum access network architecture using single thermal source for ultra-large-capacity QKD. Addresses OTP encryption rate demands in PON-based quantum access networks, achieving record capacity with passive network design. Use when designing QKD access networks, scaling quantum key distribution to multi-user networks, or evaluating passive thermal-source quantum communication.Votes: 0GitHub stars: 3
- Qml Mutation TestingSystematic mutation testing methodology for quantum machine learning models. Use when testing, validating, or assessing robustness of quantum ML models, variational quantum circuits, and QNNs.Votes: 0GitHub stars: 3
- Q Anchor Federated Quantum LearningQ-ANCHOR architecture for Quantum Federated Learning (QFL) that addresses double-drift phenomenon (client drift from non-IID data + hardware bias from noisy quantum gradients). Uses ZNE-guided server anchoring and stateful client correction. Proves convergence under noisy quantum gradient estimates. Activation: Q-ANCHOR, federated quantum learning, QFL, zero-noise extrapolation, quantum federated aggregation, quantum hardware bias, client drift, non-IID quantum dataVotes: 0GitHub stars: 3
- Predictive Maintenance Uncertainty ScenarioScenario-based optimization framework for predictive maintenance scheduling under uncertainty. Integrates calendar-based, usage-based, and condition-monitoring (RUL) information into unified finite-horizon decision framework. Use when: (1) optimizing multi-asset maintenance schedules, (2) dealing with uncertain RUL estimates, (3) comparing expected-cost vs tail-risk maintenance policies, (4) integrating heterogeneous maintenance information sources, (5) scenario-based decision making for asse...Votes: 0GitHub stars: 3
- Pqc Edge Federated IomtPost-Quantum Cryptography (PQC) integration pattern for Federated Learning (FL) in Internet of Medical Things (IoMT) environments. Covers edge-native orchestration, PQC key establishment, distributed cryptographic processing, and Kubernetes-based scalable frameworks validated on resource-constrained hardware. Activation: post-quantum iomt, pqc federated learning, quantum-resistant healthcare, edge-native cryptography, medical device security, ml-kem ml-dsa healthcare, kubernetes iomt, distrib...Votes: 0GitHub stars: 3
- Post Training In Time Series Foundation Models A USkill generated from arXiv paper 2607.20002: Post-Training in Time Series Foundation Models: A Unifying FrameworkVotes: 0GitHub stars: 3
- Pinn Small Signal Stability Multi InverterPhysics-informed neural network for small-signal stability analysis in multi-inverter power systems — predicts poles/residues of whole-system impedance across full operating space, identifies oscillation risks and optimal generation distribution.Votes: 0GitHub stars: 3
- Le Critique Privileged Value FunctionsPrivileged Value Functions for LLM reinforcement learning.Votes: 0GitHub stars: 3
- From Data To Actions In Intelligent Transportation Systems A Prescription Of Functional Requirements For Model Actionability**arXiv ID:** 2002.02210 **Authors:** Ibai Lana, Javier J. Sanchez-Medina, Eleni I. Vlahogianni, Javier Del Ser **Published:** 2020-02-06T12:02:30Z **Abstract:** Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that {are} data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a ``story'' intensively producing and consuming large amounts of data. A~diversity o...Votes: 0GitHub stars: 3
- Fast Whole Brain Spectralot AlignmentFunctional alignment method for fMRI using SpectralOT to embed cortical geometry into Laplace-Beltrami eigenmodes for cross-subject decodingVotes: 0GitHub stars: 3
- Bcijelly Integrated Ecosystem BciBCIJelly: integrated BCI research ecosystem.Votes: 0GitHub stars: 3
- Omni Sleep FoundationOmni-Sleep sleep foundation model methodology using CNS/ANS hierarchical contrastive learning for topology-constrained multimodal PSG representation learning. Use when working with sleep staging, affective BCI, polysomnography analysis, CNS-ANS dynamics, multimodal biosignal foundation models, or physiological hierarchy in representation learning.Votes: 0GitHub stars: 3
- Off Switch GramMethodology from Anthropic's "An off switch for dual-use knowledge in AI models" (Jul 2026). Use when you need to surgically remove or gate specific capabilities/knowledge (dual-use, dangerous, or disallowed) from a model while preserving general performance, and want that removal to RESIST restoration via fine-tuning. Based on Gradient-Routed Auxiliary Modules (GRAM) — isolating a capability in a removable module and deleting it.Votes: 0GitHub stars: 3
- Off Switch Dual Use Knowledge ControlOff switch for dual-use knowledge using GRAM methodology.Votes: 0GitHub stars: 3
- VllmServes LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.Votes: 0GitHub stars: 3
- Travel Oriented Reasoning Large Language Model ViaDerived from arXiv:2606.29254 - Travel-Oriented Reasoning Large Language Model via Domain-Specific Knowledge GraphsVotes: 0GitHub stars: 3
- Transformerguided Swarm Intelligence For Frugal Neural Architecture Search**arXiv ID:** 2607.11826 **Authors:** Romain Amigon **Published:** 2026-07-13T17:18:24Z **Abstract:** Neural Architecture Search (NAS) has automated the design of deep learning models but traditionally requires massive computational resources, often measured in thousands of GPU-days. In this paper, we propose a frugal and memetic NAS framework designed to democratize architecture design on consumer-grade hardware. Our approach combines the global macro-search capabilities of an autoregressive...Votes: 0GitHub stars: 3