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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Quantum Analog Encoding FinanceQuantum analog-encoding methodology for correlated Gaussian vectors and rough volatility simulation. Uses QSVT framework to prepare quantum states representing correlated Gaussian distributions with O(log N) gate complexity. Applicable to financial modeling (rough Bergomi), quantum ML data loading, and probabilistic simulation. Use when working with quantum state preparation for financial distributions, rough volatility models, or QSVT-based matrix functions.Votes: 0GitHub stars: 3
- Quantum Annealer Pipeline AuditCritical audit methodology for quantum annealer portfolio optimization pipelines. Reveals structural failures in standard penalty-encoded QUBO formulations (chain-break fractions 83-92% on D-Wave Pegasus/Zephyr) and quantifies actual QPU usage in hybrid services (0.7% of runtime). Use when: (1) auditing quantum advantage claims, (2) designing penalty-free QUBO formulations, (3) evaluating D-Wave hybrid services, (4) portfolio optimization with quantum annealing, (5) comparing quantum vs class...Votes: 0GitHub stars: 3
- Quantum Annealing XaiQuantum annealing-based feature selection for interpretable AI in Convolutional Neural Networks. Uses constrained optimization to select most important feature maps contributing to predictions, providing explainable AI with improved class disentanglement. Use when implementing XAI for CNNs, quantum annealing feature selection, or model interpretation via quantum computing.Votes: 0GitHub stars: 3
- Quantum Autoencoder Mri AnomalyQuantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI. Uses angle encoding, variational encoder-decoder with trash qubits, and incompressibility-based anomaly scoring. Achieves ROC-AUC ~0.95 slice-level and ~0.813 patch-level with spatially localized anomaly heatmaps. Use when: quantum anomaly detection, brain MRI analysis, quantum autoencoder design, compression-based medical diagnostics, trash qubit encoding, variational quantum encoders.Votes: 0GitHub stars: 3
- Quantum Bayesian State EstimationQuantum algorithms for Bayesian state estimation and transport dynamics prediction. Use when: (1) implementing Bayesian filtering/prediction on quantum computers, (2) solving Fokker-Planck equations via quantum algorithms, (3) encoding probability distributions in quantum state amplitudes, (4) implementing quantum Fourier transform for spectral-domain evolution, (5) using Wick rotation for quantum simulation of diffusion. Activation: quantum Bayesian estimation, Fokker-Planck quantum solver, ...Votes: 0GitHub stars: 3
- Quantum Block Encoding Difference Of GaussianQuantum block encoding methodology for Difference-of-Gaussian (DoG) operators on periodic grids. Implements Linear Combination of Unitaries (LCU) framework without black-box oracles. Activation: quantum block encoding, DoG operator, quantum machine learning, quantum signal processing.Votes: 0GitHub stars: 3
- Quantum Boltzmann Machine BilevelQuantum Boltzmann Machine via Bilevel Optimization methodology. Extends QAOA circuit to bilevel optimization for fully connected QBMs, overcoming the fixed target Hamiltonian barrier. Use when building quantum generative models, training quantum Boltzmann machines, or extending QAOA for ML applications. arXiv:2605.07473Votes: 0GitHub stars: 3
- Quantum Cayley Llm AdaptersQuantum-enhanced LLM methodology using Cayley-parameterized unitary adapters to overcome classical memory scaling limits. Enables quantum circuit blocks in frozen transformer architectures for LLM fine-tuning on real quantum hardware.Votes: 0GitHub stars: 3
- Quantum Classical Hybrid Imaging[TODO: Complete and informative explanation of what the skill does and when to use it. Include WHEN to use this skill - specific scenarios, file types, or tasks that trigger it.]Votes: 0GitHub stars: 3
- Quantum Classical Hybrid NnDesign and implement quantum-classical hybrid neural networks that combine quantum computing with classical deep learning. Use when working with quantum machine learning (QML), quantum neural networks (QNN), hybrid quantum-classical architectures, or when needing to balance quantum expressivity with trainability. Keywords: quantum ML, quantum neural network, hybrid quantum-classical, QEEGNet, LieTrunc-QNN, quantum computing for AI, quantum-classical normalizing flows.Votes: 0GitHub stars: 3
- Quantum Classical Uncertainty Electromagnetism经典与量子电磁理论中的不确定性关系。推导位置和动量（波矢）空间中方差尖锐不确定关系，统一形式ΔrΔk≥5/2适用于经典光束、量子相干光束和单光子理论。揭示经典-量子对应中的不确定性结构。Votes: 0GitHub stars: 3
- Quantum Cloning Learning EquivalenceQuantum cloning-learning equivalence methodology — proves that for structured quantum state classes (n-qubit stabilizer states), the optimal sample complexity of cloning equals that of learning: Theta(n). Uses Abelian State Hidden Subgroup framework and random purification channels to connect quantum cloning to classical sample amplification. Bridges No-Cloning theorem foundations with quantum learning theory and cryptography. Activation: quantum cloning, quantum learning theory, stabilizer s...Votes: 0GitHub stars: 3
- Quantum CognitionQuantum cognition methodology for modeling cognitive processes using quantum probability theory. Combines neuroscience insights with quantum information formalism to model decision making, context-dependent reasoning, mental state dynamics, and non-classical cognitive correlations. Use when: (1) modeling cognitive phenomena that violate classical probability (order effects, conjunction fallacy), (2) building quantum-like models of decision making, (3) analyzing contextuality in mental represe...Votes: 0GitHub stars: 3
- Quantum Compiler RoutingQuantum compiler qubit mapping and routing methodology for scalable quantum circuit compilation. Covers position graph abstraction, heuristic mapper optimization (SABRE), memoized congestion resolution, and architecture-aware compilation for heterogeneous quantum hardware including TI-QCCD (Trapped-Ion Quantum Charge-Coupled Device) systems. Use when: implementing quantum compilers, designing qubit mapping and routing algorithms, optimizing quantum circuit compilation for specific hardware ar...Votes: 0GitHub stars: 3
- Quantum Complexity Math StructureQuantum computing complexity theory + mathematical structure analysis. Analyzes quantum circuits, algorithms, and information theory through mathematical frameworks including Lie algebra, complexity classes, and geometric methods. Use when studying: quantum circuit complexity, geometric quantum computing, Lie algebra in quantum neural networks, or mathematical foundations of quantum information.Votes: 0GitHub stars: 3
- Quantum Computational SensingQuantum computational sensing (QCS) methodology for task-specific information extraction combining quantum sensing with quantum computing. Use for binary classification sensing tasks, quantum-enhanced signal processing, and quantum-classical hybrid sensing systems. Keywords: quantum sensing, quantum computational sensing, QCS, superconducting circuits, parameterized quantum circuits, quantum machine learning, displacement sensing, quantum-enhanced classification.Votes: 0GitHub stars: 3
- Quantum Computing PatternsReusable patterns from quantum computing and quantum machine learning research. Covers distributed quantum computing, variational quantum algorithms, QML architectures, and quantum advantage verification. Use when analyzing quantum computing papers, designing quantum-classical hybrid systems, or researching quantum advantage in ML. Triggers: quantum computing, QML, variational quantum algorithm, distributed quantum, quantum advantage, quantum circuit routing, NISQ.Votes: 0GitHub stars: 3
- Quantum Control EngineeringEngineering patterns for reliable, efficient quantum control systems. Covers pulse-level gate optimization, real-time closed-loop QEC, dynamic decoder scheduling, physics-informed LLM control, and thermodynamic control optimization. Use when designing quantum control architectures, optimizing gate implementations, building real-time error correction systems, or managing quantum resource allocation. Keywords: quantum control, pulse optimization, QEC scheduling, fault-tolerant control, FPGA qua...Votes: 0GitHub stars: 3
- Quantum Control Latent ManifoldEnd-to-end learning of quantum control on latent dynamical manifold using LSTM. Joint learning of system dynamics and control strategies in low-dimensional latent space, replacing iterative simulate-then-optimize paradigm. Activation: end-to-end quantum control, latent manifold learning, quantum control LSTM, adiabatic speedup, spin chain state transfer.Votes: 0GitHub stars: 3
- Quantum Control Meta Learning ScalingScaling laws for meta-learning in quantum control — determining when adaptation justifies its overhead, few-shot pre-adaptation budget estimation, and OOD robustness patterns. Covers device heterogeneity, environmental drift, per-device calibration reduction, and adaptation gain saturation. Activation: quantum control meta-learning, adaptation scaling laws, quantum gate calibration, per-device calibration, out-of-distribution quantum control, meta-learning quantum.Votes: 0GitHub stars: 3
- Quantum Control Pulse SoftwareSoftware framework methodology for pulse-level quantum computing that bridges gate-based abstractions with hardware-aware optimization. Integrates quantum optimal control within quantum machine learning (QML), enabling composable ansatz constructions, end-to-end pulse parameter optimization, and Fourier-analytic diagnostics. Based on QML-Essentials package with JAX-based high-performance implementation.Votes: 0GitHub stars: 3
- Quantum Cost Landscape RavinesRavine analysis framework for Quantum Cost Landscapes (QCLs) using Nudged Elastic Band (NEB) algorithm. Identifies low-cost paths connecting local minima in VQA optimization, constructs ensemble predictions from ravine-structured QNNs, and introduces a resource-light pre-training metric for VQA performance prediction. Use when optimizing variational quantum algorithms, avoiding barren plateaus, or building QNN ensembles.Votes: 0GitHub stars: 3
- Quantum Cv Learning TheoryQuantum learning theory for continuous-variable (bosonic) systems. Covers sample complexity analysis for learning non-Gaussian and Gaussian states, trace distance bounds via covariance matrices, Gaussian state testing, and efficient Gaussian process learning protocols. Use when: quantum learning theory, continuous variable, bosonic systems, Gaussian states, quantum tomography, sample complexity quantum, CV quantum, quantum state learning, quantum optical systems, bosonic quantum learning.Votes: 0GitHub stars: 3
- Quantum Data Centers EntanglementQuantum data center network design and entanglement distribution optimization. Analyze resource requirements for entanglement purification in multi-hop quantum networks.Votes: 0GitHub stars: 3
- Quantum Data Management PhysicsToolbox methodology for understanding the physics of quantum data management. Connects quantum device physical behavior to database problem structure and difficulty, evaluates quantum annealing for combinatorial optimization in data management. Use when analyzing quantum computing for database systems, evaluating quantum annealing for data management tasks, or studying physics-database problem mappings.Votes: 0GitHub stars: 3
- Quantum Diagnostic RobustnessQuantum-based diagnostic architecture methodology for robust medical image analysis using compact quantum feature representations. Combines quantum-inspired architectures with classical deep learning for enhanced diagnostic accuracy with fewer parameters. Activation: quantum diagnostics, robust medical imaging, quantum-based architecture, diagnostic classification, quantum compact modelVotes: 0GitHub stars: 3
- Quantum Differential Privacy GeometryFramework for analyzing how quantum entanglement reshapes the geometry of quantum differential privacy, characterizing privacy-utility tradeoffs in quantum information processing systems.Votes: 0GitHub stars: 3
- Quantum Distributed Database SearchLow-depth distributed quantum search algorithms for unordered database lookup. Splits Grover search across distributed quantum nodes to reduce circuit depth and NISQ noise. Use when implementing distributed quantum computing for database search, optimizing Grover algorithm circuit depth, or designing low-depth exact quantum search on NISQ hardware.Votes: 0GitHub stars: 3
- Quantum Distributed SnapshotQuantum distributed computing algorithms based on classical snapshot theory. Extends Chandy-Lamport snapshot to quantum systems for implementing decomposable global quantum operations. Use when designing quantum distributed algorithms, quantum causality analysis, quantum consensus, or quantum snapshot operations. Keywords: quantum distributed systems, QGO algorithm, quantum causality, quantum snapshot, Chandy-Lamport quantum.Votes: 0GitHub stars: 3
- Quantum Divide Conquer OptimizationQuantum divide and conquer methodology combining classical dynamic programming with quantum search to achieve improved exponential base for NP-hard combinatorial optimization. Parameterized hybrid algorithms with tunable quantum-classical balance. Use when designing quantum algorithms for NP-hard problems, implementing divide-and-conquer quantum strategies, or combining quantum search with classical DP.Votes: 0GitHub stars: 3
- Quantum Dl Feasibility AssessmentAssess whether Quantum Deep Learning (QDL) approaches can practically deliver advantages given current and projected hardware constraints. Based on systematic survey of quantum algorithms mapped to deep learning applications. Use when: (1) Evaluating QDL proposals for practical viability, (2) Estimating qubit/gate requirements for quantum neural networks, (3) Deciding between quantum vs classical approaches for ML tasks, (4) Research planning in quantum machine learning. Triggers: quantum dee...Votes: 0GitHub stars: 3
- Quantum Dot Reservoir ComputingGeometric approach to zero-memory quantum dot reservoir computing — leverages intrinsic nonlinear dynamics of quantum dot arrays for temporal information processing without internal memory states. Use when working with quantum dot systems for reservoir computing, neuromorphic computing with quantum materials, zero-memory temporal processing, or geometric approaches to quantum machine learning (arXiv: 2606.29320)Votes: 0GitHub stars: 3
- Quantum Drug DiscoveryAnalysis skill for quantum computing in drug discovery and molecular simulation. Use when researching quantum algorithms for molecular dynamics, quantum ML for drug screening, quantum chemistry methods (DFT, QM/MM), or quantum optimization for drug design. Triggers: quantum drug discovery, quantum molecular simulation, quantum chemistry, quantum pharmacology, quantum screening.Votes: 0GitHub stars: 3
- Quantum Economic Action ConstantQuantum economics methodology using economic action constant (hbar_E) as structural analogue to Planck's constant for modeling macroeconomic regime transitions under radical uncertainty.Votes: 0GitHub stars: 3
- Quantum Encoding SelectionQuantum machine learning data encoding selection methodology based on arXiv:2606.05387. Provides a three-axis taxonomy (cost-expressivity-robustness), depth-fidelity bounds under NISQ decoherence, and a five-regime decision framework for choosing optimal encoding strategies.Votes: 0GitHub stars: 3
- Quantum End To End Learning QelQuantum End-to-End Learning (QEL) methodology for contextual combinatorial optimization. First quantum computing-based end-to-end learning framework leveraging QAOA with context re-uploading phase-separator. Enables joint end-to-end training with stationarity guarantee, avoiding NP-hard optimization solvers. Use when: (1) solving contextual combinatorial optimization problems, (2) implementing quantum ML for decision-making under uncertainty, (3) combining QAOA with end-to-end learning, (4) d...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 Enhanced Distributed SensingQuantum-enhanced distributed network sensing (DQN) using multiple quantum resources: catalysis, entanglement, and squeezing for multiphase estimation approaching Heisenberg limit. arXiv: 2605.19545.Votes: 0GitHub stars: 3
- Quantum Enhanced Svm Financial PredictionHybrid quantum-classical SVM methodology using quantum kernel methods for financial market prediction and pattern recognition in high-dimensional data. Use when building quantum ML models for financial forecasting, market prediction, or trading strategy optimization.Votes: 0GitHub stars: 3
- Quantum Entanglement Distributed StorageQuantum entanglement-assisted distributed storage methodology — achieving 2x bandwidth reduction for oblivious updates using shared entanglement and CSS codes.Votes: 0GitHub stars: 3
- Quantum Entanglement Pet ImagingMethodology for quantum entanglement degree imaging using PET scanners — extracting C_QE biomarkers from annihilation photon polarization correlations via Compton scattering. Use when researching quantum entanglement medical imaging, PET biomarker development, positronium imaging, or polarization-correlated photon diagnostics.Votes: 0GitHub stars: 3
- Quantum Entanglement VerificationQuantum entanglement verification methodology — detecting fake entanglement from imperceptible measurement deviations, with implications for quantum information security, quantum key distribution, and entanglement-based protocols.Votes: 0GitHub stars: 3
- Quantum Erasure ImagingQuantum Erasure Imaging (QEI) methodology — turns delayed-choice quantum erasure into practical dual-modality imaging protocol. Simultaneously reconstructs absorption T(x,y) and phase-sensitive quadrature from a single entangled photon run. Use when: quantum imaging, delayed-choice erasure imaging, dual-modality quantum sensing, entangled photon imaging, quantum microscopy, quantum medical imaging, phase-sensitive detection.Votes: 0GitHub stars: 3
- Quantum Error Correction Gauge TheoryQuantum error correction using gauge theories and quantum reference frames. Building QECC from lattice gauge theories (QED, QCD). Use when researching quantum error correction, gauge theory applications, or quantum computing reliability.Votes: 0GitHub stars: 3
- Quantum Error Correction MethodsReusable patterns from quantum error correction research. Covers RL-controlled QEC, fault-tolerant architectures, neutral-atom systems, Bacon-Shor codes, and loss-biased codes. Use when analyzing QEC papers, designing fault-tolerant quantum systems, selecting error correction codes, or comparing QEC approaches.Votes: 0GitHub stars: 3
- Quantum F Divergence ContractionQuantum f-divergence contraction rate analysis methodology. Use when analyzing quantum channel convergence, strong data processing inequalities (SDPI), quantum information contraction bounds, or studying how quantum states approach equilibrium under noisy channels.Votes: 0GitHub stars: 3
- Quantum Fault Tolerance BenchmarkEvaluate quantum error-correcting codes under hardware-motivated and biased noise models. Benchmark fault-tolerant quantum computing primitives via noisy stabilizer simulation. Use when: (1) evaluating QEC code performance, (2) designing fault-tolerant quantum circuits, (3) simulating quantum error correction under realistic noise, (4) comparing logical error rates across hardware platforms, (5) FTPrimitiveBench methodology.Votes: 0GitHub stars: 3
- Quantum Fault Tolerance VerificationQuantum fault-tolerance verification methodology using symbolic execution for quantum error correction codes. Formal verification framework for proving fault-tolerance properties of QECC implementations. Use when analyzing quantum error correction, verifying fault-tolerance properties, or implementing quantum programs. Activation: quantum fault tolerance, QECC verification, quantum error correction, quantum symbolic execution.Votes: 0GitHub stars: 3
- Quantum Feature Amplification NetworkQuantum Feature Amplification Network (QFAN) methodology for autoregressive quantum generative modeling with fixed qubit budget. Use when generating quantum images, designing quantum generative models, or reducing qubit requirements for quantum demonstrations.Votes: 0GitHub stars: 3
- Quantum Feature AmplificationQuantum Feature Amplification Network (QFAN) methodology for autoregressive quantum generative modeling. Decouples quantum register size from output dimension using fixed-size quantum circuits combined with classical autoregressive decoding. Use when designing scalable quantum generative models for high-dimensional data, quantum ML for scientific simulations, or hybrid quantum-classical generative architectures. arXiv: 2605.16044Votes: 0GitHub stars: 3