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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Quantum Linear System ResidualResidual-based quantum linear system algorithm with dynamic stopping methodology. Use when solving linear systems Ax=b on quantum computers, implementing HHL-type algorithms, quantum PDE solvers, or designing efficient quantum algorithms with adaptive precision control.Votes: 0GitHub stars: 3
- Quantum Logic Ai FinanceQuantum logic framework for human-centric AI in finance, extending classical rationality to contextual reasoning using quantum-inspired neural networks for algorithmic trading, portfolio management, and robo-advisory. Based on arXiv:2510.05475v1.Votes: 0GitHub stars: 3
- Quantum Logic Finance AiMethodology for applying quantum logic to human-centric AI in finance — moving from classical rationality to contextual reasoning. Explores quantum-inspired neural networks for financial statement analysis, algorithmic trading, portfolio management, and robo-advisory services. Advocates for broader exploration of quantum-inspired models that capture context-dependent decision making in financial domains.Votes: 0GitHub stars: 3
- Quantum Machine Learning Uav Anomaly DetectionLeakage-free evaluation of quantum ML for UAV anomaly detection. Group-aware temporal protocol + three-mode feature audit + hybrid XGBoost-DRU classifier.Votes: 0GitHub stars: 3
- Quantum Magic State AnalysisAnalyze quantum algorithms by quantifying magic (non-stabilizerness) as the core quantum resource. Connect magic generation to number-theoretic complexity in Shor's algorithm and other quantum routines. Use when analyzing quantum algorithm resource costs, evaluating quantum advantage beyond gate counts, studying magic state distillation, or linking quantum resource theory to computational hardness. arXiv: 2605.05347Votes: 0GitHub stars: 3
- Quantum Margulis CodesQuantum Margulis Codes methodology for fault-tolerant quantum computation. A new class of QLDPC codes derived from Margulis classical LDPC construction via two-block group algebra (2BGA) framework. Unlike bivariate bicycle codes, these can be efficiently decoded with standard min-sum decoder (linear complexity) under code capacity noise model. Use when: QLDPC code design, quantum error correction codes, fault-tolerant quantum computing, LDPC code construction, girth-controlled codes, or min-s...Votes: 0GitHub stars: 3
- Quantum Market EntanglementQuantum market stabilization via entangled neural traders methodology. Uses quantum entanglement between traders' valuations as endogenous mechanism to mitigate runaway devaluation in speculative busts. RL agents with quantum-correlated qubit-encoded valuations stabilize prices and increase net worth vs classical markets. Quantized p-guessing game shows entanglement eliminates pathological Nash equilibrium driving market collapse. Use when: (1) modeling financial market dynamics with quantum ...Votes: 0GitHub stars: 3
- Quantum Markovian Stochastic FrameworkDouble Covariance Model (DCM) stochastic subquantum framework for deriving macroscopic quantum Markovian dynamics from microscopic correlated fluctuations. Extends DCM to interacting multi-particle systems. Use when: stochastic quantum mechanics, open quantum systems modeling, quantum Markov processes, subquantum theories, quantum statistical mechanics, deriving master equations from stochastic processes, quantum-classical boundary modeling.Votes: 0GitHub stars: 3
- Quantum Math System EngineeringCross-disciplinary methodology for quantum computing combining mathematical theory, system engineering, and multi-objective optimization. Use for quantum neural architecture search, distributed quantum compilation, entropy geometric analysis, and quantum system optimization.Votes: 0GitHub stars: 3
- Quantum Mechanical Data AssimilationQuantum Mechanical Data Assimilation (QMDA) methodology for combining dynamical models with partial, noisy observations. Uses operator-theoretic framework (Koopman/transfer operators) for uncertainty representation, forecast propagation, and assimilation updates. Compare with DATO (Data Assimilation with Transfer Operators) for system state inference. Use when: assimilating noisy/sparse observations into dynamical models, comparing classical vs quantum assimilation paradigms, operator-based s...Votes: 0GitHub stars: 3
- Quantum Medical AiQuantum machine learning for medical and healthcare applications. Covers quantum kernel methods for medical imaging, hybrid quantum-classical models for clinical prediction, quantum knowledge graphs for medical reasoning, and quantum neural networks for diagnostics. Use when researching or implementing quantum advantage in medical AI, healthcare prediction, clinical diagnostics, medical foundation model embeddings, or quantum-enhanced drug discovery.Votes: 0GitHub stars: 3
- Quantum Medical Diagnosis PatternsReusable patterns for building hybrid quantum-classical Medical AI diagnosis systems — combining quantum ML, classical ML, and medical domain knowledge.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 Medical DiagnosticsPatterns and methodologies for applying quantum computing and quantum machine learning to medical diagnostics, healthcare, and clinical applications. Covers hybrid quantum-classical architectures (HQNNs, QNNs, QSVMs), quantum-enhanced medical imaging, federated quantum learning for privacy-aware diagnosis, and parameter-efficient quantum multi-task learning. Use when: (1) researching quantum ML for healthcare/medical diagnosis, (2) designing hybrid quantum-classical models for medical image c...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 ImagingAnalysis and research synthesis skill for quantum-enhanced medical imaging papers. Use when working with papers on quantum computing for medical image reconstruction (MRI/CT/PET), quantum sensors for diagnostics (NV centers, quantum dots), or quantum algorithms in radiology. Triggers: quantum medical imaging, quantum radiology, quantum MRI, quantum sensors medicine, quantum diagnostics.Votes: 0GitHub stars: 3
- Quantum Medical PatternsReusable research patterns from quantum ML in healthcare: hybrid modeling, entanglement PET (J-PET), CV photonic QNNs, quantum autoencoders, quantum ophthalmology, generative models, kernel methods, clinical trials, tensor-network federated learning, TSHF fusion. 4 sub-class skills exist. Triggers: quantum medical, entanglement PET, CV-QNN, quantum autoencoder, quantum ophthalmology, J-PET, 量子医疗.Votes: 0GitHub stars: 3
- Quantum Medical ResearchResearch methodology for quantum computing applications in medicine and healthcare. Covers quantum machine learning for medical imaging, drug discovery, clinical trial optimization, disease diagnosis, and precision medicine. Use when researching or implementing quantum-enhanced healthcare solutions, hybrid quantum-classical models for biomedical data, or quantum algorithms for molecular simulation and drug discovery. Trigger words: quantum medical, quantum healthcare, quantum drug discovery, ...Votes: 0GitHub stars: 3
- Quantum Minimax EstimationMinimax estimation of high-order functionals using quantum computing arguments. Bridges quantum information theory with statistical estimation theory, providing sample-optimal bounds for functional estimation. Activation: minimax estimation, quantum arguments, high-order functionals, statistical estimation, quantum information theory, sample complexity, functional estimation, quantum statisticsVotes: 0GitHub stars: 3
- Quantum Mirror TomographyQuantum mirror-based continuous-variable state tomography methodology. Transfers complete photonic state information onto a control atomic system for full characterization through kernel functions, direct wavefunction reconstruction, and pointwise Wigner function measurements. Overcomes exponential sample complexity of conventional CV tomography.Votes: 0GitHub stars: 3
- Quantum Ml Advantage NoisyMethodology for demonstrating quantum machine learning advantage with tens of noisy qubits. Evaluates coherent quantum processing vs fixed-measurement schemes under realistic hardware noise (gate errors, readout errors, coherence times). Use when assessing QML advantage feasibility on NISQ devices, designing quantum-classical learning benchmarks, or evaluating data acquisition bottlenecks in quantum ML. Keywords: quantum ml advantage, noisy qubits, qml benchmark, coherent processing, quantum ...Votes: 0GitHub stars: 3
- Quantum Ml CertificationMethodology for certified and robust quantum machine learning. Combines Interval Bound Propagation (IBP) for certified robustness training of QNNs, conformal prediction for distribution-free uncertainty quantification, and Lindblad dynamics learning for quantum noise characterization. Use when: building quantum neural networks with robustness guarantees, quantifying uncertainty in quantum ML predictions, characterizing quantum processor noise via ML, or implementing certified quantum training...Votes: 0GitHub stars: 3
- Quantum Ml Certified TrainingCertified training methodology for quantum machine learning models using interval bound propagation (QIBP). Use when building, training, or certifying robustness of quantum neural networks against adversarial perturbations.Votes: 0GitHub stars: 3
- Quantum Ml Data LoadingQuantum machine learning data loading optimization - efficient quantum state preparation, amplitude encoding, and data embedding techniques for QML. Use when: (1) Loading classical data into quantum circuits for QML, (2) Optimizing quantum feature maps and kernels, (3) Implementing efficient amplitude encoding, (4) Reducing circuit depth for data embedding, (5) Designing quantum data loaders for NISQ devices.Votes: 0GitHub stars: 3
- Quantum Ml Ground State Measurement基于SIC-POVM测量空间的量子基态变分学习方法。使用自回归神经网络（GRU）编码SIC-POVM测量结果的概率分布，通过梯度下降最小化能量并强制物理性约束（正性层级条件）。适用于量子多体基态求解、变分量子态制备。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 Ml Logical Processor BenchmarkBenchmarking quantum machine learning on logical vs physical quantum processors — end-to-end validation of fault-tolerant quantum kernel methods for solving differential equations on neutral-atom hardware. Activation: quantum benchmark, logical processor, quantum differential equations, quantum kernel ML, neutral-atom quantum computing, fault-tolerant ML.Votes: 0GitHub stars: 3
- Quantum Ml PatternsReusable patterns for Quantum Machine Learning (QML) research and implementation. Covers Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), Quantum Approximate Optimization Algorithm (QAOA), quantum kernels, QUBO-encoded RL policy search, and hybrid quantum-classical training. Use when analyzing QML papers, designing variational quantum algorithms, implementing quantum-classical hybrid systems, or selecting quantum data encoding strategies. Trigger: quantum ML, QML, variationa...Votes: 0GitHub stars: 3
- Quantum Ml ResearchQuantum Machine Learning research assistant. Searches arxiv for quantum ML papers, analyzes patterns from knowledge graph (kg.db), extracts concepts from quantum circuits, neural networks, and finance/medical applications. Use when researching quantum computing applications, quantum algorithms, quantum portfolio optimization, quantum Monte Carlo, quantum neural networks, hybrid quantum-classical medical classification, or analyzing quantum ML literature. Activation: quantum ML research, quant...Votes: 0GitHub stars: 3
- Quantum Ml RobustnessAnalyze and test Quantum Machine Learning (QML) model accuracy and robustness. Covers quantum neural network (QNN) robustness evaluation, mutation testing for quantum circuits, variational quantum circuit analysis, and scalability assessment for near-term quantum hardware. Use when evaluating QML model reliability, designing fault injection tests for quantum circuits, assessing QNN generalization, or preparing quantum algorithms for NISQ-era hardware deployment. Triggers: quantum ML robustnes...Votes: 0GitHub stars: 3
- Quantum Ml Simulation Learning ComparisonMethodology for empirically comparing classical simulation versus sample-based learning of quantum systems. Uses complexity-theoretic analysis of simulability vs learnability, Born-rule statistics reproduction, and empirical benchmarks. Applicable to quantum system characterization, quantum advantage verification, and hybrid quantum-classical algorithm design. Activation: quantum simulation vs learning, sample-based quantum learning, simulability learnability quantum, quantum system character...Votes: 0GitHub stars: 3
- Quantum Ml Statistics Invariant Theory基于不变量理论的iPCA模型MLE存在性检验算法。利用quiver半不变量技术建立MLE存在的充分必要条件，适用于任意维向量。提供基于Derksen-Weyman算法的可计算检验工具。连接统计学与不变量理论。Votes: 0GitHub stars: 3
- Quantum Modular Forms PartitionsNumber theory partition statistics methodology connecting restricted excludant statistics in parity-distinct partitions with quantum modular forms via q-series transformations.Votes: 0GitHub stars: 3
- Quantum Network ControlOptimize entanglement distribution in quantum networks via link-layer control strategies. Compare sequential vs simultaneous entanglement swapping for multi-hop quantum communication. Use when: (1) designing quantum network architectures, (2) optimizing entanglement distribution, (3) comparing quantum repeater strategies, (4) quantum internet protocol design, (5) entanglement swapping optimization, (6) quantum network link-layer control.Votes: 0GitHub stars: 3
- 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 Network SchedulingQuantum network resource allocation and entanglement flow scheduling. Use when designing, optimizing, or analyzing quantum network architectures involving entanglement distribution, multi-channel resource allocation, queuing mechanisms for quantum requests, or classical allocation algorithms (Dynamic Efficient, Longest Queue First, Weighted LQF) applied to quantum networks. Also relevant for quantum-classical hybrid system scheduling and entanglement routing in distributed quantum computing.Votes: 0GitHub stars: 3
- Quantum Network Task ControlCentralized task-based quantum network control framework. Resource-centric approach replacing layered protocol stacks. Centralized controller tracks quantum memory availability across nodes and schedules objectives via priority-based scheduler. Use when: quantum network architecture, centralized quantum control, task-based quantum networking, quantum memory scheduling, SeQUeNCe simulator, quantum network scaling, arXiv:2605.03336.Votes: 0GitHub stars: 3
- Quantum Neural Architecture SearchQuantum Neural Network Architecture Search (QNAS) skill for designing efficient quantum neural networks on NISQ hardware. Uses multi-objective optimization (NSGA-II) to balance accuracy, runtime efficiency, and circuit cutting overhead. Apply when designing quantum neural networks, optimizing hybrid quantum-classical architectures, or searching for Pareto-optimal quantum circuit configurations. Keywords: quantum neural network, QNN, quantum architecture search, variational quantum circuit, an...Votes: 0GitHub stars: 3
- Quantum Neural ArchitectureQuantum Neural Network (QNN) architecture design and optimization patterns. Covers quantum-classical hybrid learning, Lie algebra truncation, barren plateau mitigation, quantum expressivity, and tensor network approaches. Activates for: QNN design, quantum neural network, quantum machine learning, quantum-classical hybrid, quantum expressivity phase transition, LieTrunc, quantum gradient descent.Votes: 0GitHub stars: 3
- Quantum Neural Barren PlateauMitigating barren plateaus in Quantum Neural Networks (QNN) via AI-driven framework and advanced initialization strategies. Research skill for NISQ-era quantum machine learning optimization, covering gradient variance analysis, submartingale-based methods, and quantum circuit training stabilization. Activation: barren plateau, QNN training, quantum neural network, gradient vanishing, NISQ optimization.Votes: 0GitHub stars: 3
- Quantum Neural DynamicsAnalyze quantum neural networks (QNNs), quantum-inspired neural architectures, and quantum dynamics inference from neural data. Use when: (1) analyzing papers on quantum neural networks, (2) evaluating quantum-inspired machine learning approaches, (3) studying quantum simulation of neural systems, (4) assessing quantum error mitigation via neural networks, (5) researching quantum-neuroscience intersections, (6) extracting patterns from quantum-ML literature.Votes: 0GitHub stars: 3
- Quantum Neural Entropy EstimationEntropy estimation in multi-qutrit quantum systems using variational quantum algorithms and classical CNNs. Combines SU(3)-inspired ansatzes for VQAs on small systems with CNN-based estimators for larger qutrit systems. Use when: (1) estimating von Neumann entropy of quantum states, (2) designing quantum state tomography alternatives, (3) building hybrid quantum-classical ML pipelines for quantum characterization, (4) comparing VQA vs classical neural network approaches for quantum system ana...Votes: 0GitHub stars: 3
- Quantum Neuromorphic ComputingQuantum neuromorphic computing framework combining quantum gates, memristive synapses, quantum cognition, optical quantum neurons, and neuromorphic quantum kernels. Use when: (1) analyzing quantum brain models, (2) implementing quantum neural networks, (3) studying quantum cognition mechanisms, (4) exploring memristive quantum synapses, (5) simulating quantum neuromorphic systems, (6) building HOM interference-based optical quantum neurons, (7) comparing neuromorphic vs parameterized quantum ...Votes: 0GitHub stars: 3
- Quantum Neuroscience Analysis量子神经科学跨学科分析方法。将量子计算方法应用于神经科学问题，包括量子神经网络(QNN)用于脑信号分析、量子图神经网络(QGNN)用于脑连接、量子算法优化神经动力学建模。激活关键词: quantum neuroscience, quantum neural network, quantum EEG, quantum brain, 量子神经科学, 量子脑科学, QNN neuroscience.Votes: 0GitHub stars: 3
- Quantum Neuroscience FusionQuantum neuroscience research skill - explores the intersection of quantum computing and neuroscience, including quantum neural networks (QNN, CV-QNN, SQNN), quantum spiking neural networks, quantum brain-inspired computing, covariant quantum error correction in biological systems, quantum photonic neural networks, quantum cognitive modeling, adversarial robustness in SQNNs, and decoherence-contraction theory. Use when searching quantum neuroscience papers, analyzing quantum-ML architectures,...Votes: 0GitHub stars: 3
- Quantum Noise Robust MetrologyQuantum metrology methodology for robust frequency estimation in noisy continuous-variable systems. Covers Hamiltonian engineering with squeezing, non-Markovian environment exploitation, and quantum Fisher information optimization. Based on arXiv:2605.06263.Votes: 0GitHub stars: 3
- Quantum Nonautonomous Ode Simulation量子算法模拟非自治线性ODE的非幺正动力学。通过SVD分解将非幺正传播子写为酉算子之和，在量子硬件上直接执行膨胀，无需预知传播子闭式解。适用于开放量子系统、经济建模、非幺正动力学模拟。Votes: 0GitHub stars: 3
- Quantum Number Theory AlgorithmsQuantum algorithms for number theory problems. Use when exploring quantum approaches to: (1) primality testing, (2) factorization, (3) prime number theorem, (4) Goldbach conjecture, (5) quantum integer arithmetic, (6) Riemann zeta connections to quantum systems, or when implementing quantum probabilistic subroutines with Grover/Shor operators.Votes: 0GitHub stars: 3
- Quantum Number TheoryQuantum algorithms and number theory intersection - explores quantum computing approaches to number theory problems (factoring, primality, discrete log) and number theory patterns in quantum physics (Riemann zeta, correlations, anomaly cancellation). Use when researching: quantum algorithms for algebraic problems, Shor's algorithm variants, quantum number operators, Prouhet-Tarry-Escott problem in quantum systems, or quantum-number theory connections.Votes: 0GitHub stars: 3
- Quantum Off Policy Evaluation PricingQuantum off-policy evaluation (OPE) methodology for insurance pricing and financial decision optimization. Applies quantum reinforcement learning, quantum IPS estimators, and variational quantum circuits to pricing problems. Based on arXiv:2605.28327 (Insurance Pricing Optimization via Off-Policy Evaluation). Activation: quantum pricing, off-policy evaluation, quantum OPE, insurance pricing optimization, quantum reinforcement learning pricing, quantum IPS.Votes: 0GitHub stars: 3