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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Quantization Spiking Neural Networks Beyond AccuracyEMD-based evaluation framework for SNN quantization that goes beyond accuracy metrics. Activation: SNN quantization, Earth Mover's Distance, temporal dynamics preservation.Votes: 0GitHub stars: 3
- Quantized Return StatisticsQuantum measurement return statistics methodology analyzing quantized mean return time under strong and weak monitoring. Connects winding number topology with statistical properties of quantum state recurrence.Votes: 0GitHub stars: 3
- Quantized Snn Hardware OptimizationQuantized Spiking Neural Network Hardware Optimization - techniques for integer-state SNNs, hardware acceleration, and energy-efficient neuromorphic computing. Activation: quantized SNN, hardware SNN, neuromorphic optimization, energy-efficient spiking network, integer-state SNN, SNN quantization.Votes: 0GitHub stars: 3
- Quantum Associative Memory PhotonicQuantum associative memory simulation on photonic processors methodology. Demonstrates Hopfield network dynamics with multi-body interactions realized via multiphoton processes on programmable photonic quantum simulators. arXiv:2605.22922Votes: 0GitHub stars: 3
- Quantum Attention HebbianDerive local Hebbian learning rules for associative memory from quantum probability flow principles, validated on quantum annealers.Votes: 0GitHub stars: 3
- Quantum Biomedical SensorsFour-generation framework for quantum biomedical sensors based on quantum resource utilization. Covers clinical translation challenges, noise limits, ensemble vs single-particle sensing. Use when: quantum biosensors, biomedical quantum sensing, clinical quantum sensors, quantum medical imaging sensors, biosensor generations, quantum resource biosensing, NV center biosensing, atomic magnetometer biomedical, quantum optical biosensing, macroscopic vs microscopic quantum sensing.Votes: 0GitHub stars: 3
- Quantum Boltzmann BilevelBuild fully connected Quantum Boltzmann Machines using bilevel optimization to overcome QAOA's fixed target Hamiltonian limitation and classical Boltzmann machines' partial connectivity constraint. Use when designing quantum generative models, energy-based quantum models, quantum sampling algorithms, or quantum optimization circuits. Triggers: quantum Boltzmann machine, QAOA, bilevel optimization, fully connected Boltzmann, quantum generative model, energy-based quantum model, quantum samplin...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 Brain ModelingQuantum brain modeling methodology integrating quantum error correction, neuromorphic computing, and quantum-inspired neural architectures. Use when designing quantum brain models, implementing covariant quantum error correction for neural systems, building quantum spiking neural networks (QSNN), quantum reservoir computing for cognitive tasks, or applying quantum-like modeling to neural dynamics.Votes: 0GitHub stars: 3
- Quantum Brain Neural ArchitectureDesign and implement brain-inspired quantum neural architectures combining Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) for pattern recognition, anomaly detection, and temporal sequence modeling. Use when the user asks about quantum neural networks, quantum-spiking hybrids, brain-inspired quantum models, QSNN, QLSTM, quantum anomaly detection, or quantum memory architectures for ML tasks.Votes: 0GitHub stars: 3
- Quantum Brain Voxel ControlQuantum-inspired neural network for vision-brain understanding using voxel controlling, phase shifting, and measurement-like projection in Hilbert space. Maps brain region connectivity via quantum-inspired modules for fMRI analysis. Use when: (1) analyzing fMRI voxel connectivity, (2) building vision-brain decoding models, (3) reconstructing images from brain signals, (4) designing quantum-inspired architectures for neuroimaging. Activation: quantum brain, vision-brain understanding, voxel co...Votes: 0GitHub stars: 3
- Quantum Circuit Construction MlMachine Learning methodology for constructing quantum circuits for sets of matrices. Uses interpretable ML to extract circuit design patterns and build quantum algorithms systematically.Votes: 0GitHub stars: 3
- Quantum Circuit Distribution OptimizationJoint qubit leasing and quantum circuit distribution optimization methodology. ILP formulation for multi-QC quantum network resource allocation, NP-completeness analysis, greedy algorithm with local search. Covers qubit allocation, gate execution placement, and inter-QC communication tradeoffs (migration vs teleportation). Activation: quantum circuit distribution, qubit leasing, quantum network optimization, JQLQCD, quantum resource allocation, distributed quantum computing.Votes: 0GitHub stars: 3
- Quantum Circuit Drug DynamicsQuantum circuit simulation of compartmental drug dynamics using variational algorithms for population pharmacokinetics. Reformulates PK/PD models as open quantum systems implemented with PennyLane circuits. Use when: quantum drug simulation, pharmacokinetic modeling, population PK/PD, variational quantum algorithms for medicine.Votes: 0GitHub stars: 3
- Quantum Circuit Spectral AnalysisSpectral analysis of quantum circuits using Circuit Harmonic Matrices. Predict quantum machine learning model performance from circuit architecture without training. Analyze circuit expressivity, trainability, and generalization capacity via frequency-domain methods. Activation: quantum circuit spectral, circuit harmonic matrix, quantum circuit analysis, QML spectral, quantum model expressivity, circuit eigenvalue, quantum neural network spectrum.Votes: 0GitHub stars: 3
- Quantum Circuit Synthesis GstGenerative quantum circuit synthesis from Gate Set Tomography (GST) data using diffusion models and set-vision transformers. Bypasses traditional two-step pipeline (GST characterization + unitary decomposition) by directly learning generative concept spaces from raw GST data. Use when synthesizing hardware-native quantum circuits, learning from gate characterization data, or building context-aware quantum compilation pipelines. Activation: quantum circuit synthesis, GST circuit generation, ha...Votes: 0GitHub stars: 3
- Quantum Cognition Machine LearningExtreme Quantum Cognition Machines (EQCM) methodology — quantum learning architectures for deliberative decision making that tolerate noisy and contradictory training data using fixed quantum dynamics with dynamical attention.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 Cognitive Tunnelling OscillatorsQuantum-tunnelling oscillator models for cognitive modelling and neural computation. Models optical illusion perception and group decision making using quantum-mechanical agents with context-dependent transitions. Use when: quantum cognition, cognitive modelling, decision making models, optical illusion perception, group decision making, quantum neural systems, quantum-tunnelling oscillators.Votes: 0GitHub stars: 3
- Quantum Compartmental PkpdQuantum circuit simulation of compartmental pharmacokinetic/pharmacodynamic (PK/PD) models using variational quantum algorithms. Reformulates classical ODE-based drug dynamics as open quantum systems. Use when: quantum pharmacokinetics, quantum PK/PD modeling, compartmental drug dynamics, variational quantum algorithms for drug simulation, population pharmacokinetics, quantum drug modeling, PennyLane PK/PD, open quantum system pharmacology.Votes: 0GitHub stars: 3
- Quantum Continual Plasticity PreservationQuantum learning models naturally preserve plasticity in continual learning due to unitary constraints confining optimization to compact manifold, unlike classical networks with unbounded weight growth leading to landscape ruggedness.Votes: 0GitHub stars: 3
- Quantum Dephasing DynamicsAnalysis of dephasing effects on quantum correlations and coherence dynamics in oscillating quantum systems. Covers quantum steering, logarithmic negativity, and coherence measures under environmental decoherence. Use when analyzing quantum system robustness to noise, studying decoherence in quantum oscillators/neutrino systems, or evaluating quantum resource preservation.Votes: 0GitHub stars: 3
- Quantum Digital Twin Cognitive MemoryDigital twin framework for quantum neuromorphic cognitive modeling - combining quantum reservoir computing with tensor networks for emotional memory and thermodynamic-aware learningVotes: 0GitHub stars: 3
- Quantum Driven Neuromorphic Million QubitQuantum-Driven Neuromorphic Computing methodology for million-qubit-scale workloads — synergistic integration of quantum computing and neuromorphic architectures for large-scale computational tasks.Votes: 0GitHub stars: 3
- Quantum Eeg EncodingQuantum-EEGNet (QEEGNet) methodology for hybrid quantum-classical EEG signal encoding and classification. Combines classical EEGNet convolutional architecture with quantum variational layers for enhanced cross-task and cross-dataset generalization. Use when: designing hybrid quantum-classical neural networks for EEG/brain signals, implementing quantum layers in biomedical signal processing, optimizing quantum advantage in neuroimaging, or building cross-dataset EEG encoders. Triggers: QEEGNet...Votes: 0GitHub stars: 3
- Quantum Eeg FoundationQuantum-enhanced EEG signal analysis and neural network foundation model skill. Implements quantum-classical hybrid architectures for brain signal processing, combining quantum encoding layers with classical EEGNet for improved feature extraction from high-dimensional EEG data. Use when developing BCI systems, EEG analysis pipelines, quantum-neuroscience applications, or quantum machine learning for brain signal processing.Votes: 0GitHub stars: 3
- Quantum Graph Neural Drug DiscoveryHybrid quantum-classical drug discovery methodology combining Quantum Graph Neural Networks (QGNN) with Variational Quantum Eigensolver (VQE) for molecular property prediction and lead compound optimization. Use when: designing quantum-enhanced drug discovery pipelines, building hybrid QGNN-VQE architectures for molecular analysis, optimizing lead compounds with quantum eigenvalue solvers, or evaluating quantum advantage in pharmaceutical ML workflows.Votes: 0GitHub stars: 3
- Quantum Hopfield Associative MemoryQuantum Hopfield associative memory methodology — photonic quantum simulation of p-body Hopfield models for associative memory retrieval and spin-glass phase analysis.Votes: 0GitHub stars: 3
- Quantum Hybrid Neural ComputingQuantum-hybrid neural computing framework for designing and implementing hybrid quantum-classical neural networks. Covers variational quantum circuits (VQC), parameterized quantum circuits (PQC), quantum neural networks (QNN), and hybrid training strategies. Use when implementing quantum-classical ML models, optimizing quantum circuits for neural tasks, or analyzing quantum advantage in deep learning.Votes: 0GitHub stars: 3
- Quantum Kernel Medical EmbeddingsQuantum kernel methods for medical AI embeddings and foundation models. Use quantum support vector machines (QSVM) with frozen embeddings from medical foundation models (MedSigLIP, RAD-DINO, ViT) for medical imaging classification tasks. Applies quantum kernel advantage over classical baselines on chest radiographs, histopathology, and other medical images. Activation: quantum kernel medical, QSVM medical imaging, quantum advantage healthcare, quantum medical classification, 量子核医疗.Votes: 0GitHub stars: 3
- Quantum Like Benchmark Context Sensitive MemoryQuantum-like benchmark framework for context-sensitive associative memory with adaptive plasticity. Provides controlled methodology for comparing quantum-like vs classical associative memory models under weak structural support, order-sensitive recall, and staged task conditions.Votes: 0GitHub stars: 3
- Quantum Like Mental MarkersQuantum-informational modeling of mental markers using the I-field (information field) approach. Applies Hilbert space formalism to model contextuality, incompatibility of mental observables, and entanglement-like correlations in cognition and decision-making. Does NOT assume physical quantum processes in the brain. Use when: quantum-like cognition, mental contextuality, decision dynamics, quantum cognition modeling, I-field theory, mental markers.Votes: 0GitHub stars: 3
- Quantum Like Neural Dynamics MarkersTesting quantum-like markers in neural dynamics methodology — investigating quantum probability signatures in brain activity patternsVotes: 0GitHub stars: 3
- Quantum Linear Reservoir BottleneckAnalysis of hidden bottleneck in classical and quantum linear reservoir computing. Identifies fundamental information processing capacity limits when reservoir features and readout are both linear. Use when: reservoir computing design, quantum reservoir computing, linear system capacity analysis, information processing capacity bounds, echo state networks, quantum machine learning architecture design.Votes: 0GitHub stars: 3
- Quantum Meg Information LimitQuantum-limited information capacity analysis for magnetoencephalography (MEG) and brain imaging. Derives fundamental bounds combining Planck's constant, metabolic power, and geometric constraints. Use when analyzing quantum limits in neuroimaging, computing information-theoretic bounds for brain measurement systems, or determining optimal sensor configurations.Votes: 0GitHub stars: 3
- Quantum Memory RlReinforcement learning for quantum processes with hidden memory. Agent interacts with environment maintaining hidden quantum states evolving via unknown quantum channels, using quantum instruments for sequential intervention. Proves O~(sqrt(K)) regret bound via optimistic maximum-likelihood estimation. Use when: designing RL agents for quantum control with memory, analyzing exploration-exploitation trade-offs in quantum systems, or studying thermodynamic cost of learning in quantum processes.Votes: 0GitHub stars: 3
- Quantum Metabolic Neuroimaging LimitMethodology for computing fundamental quantum-metabolic limits on noninvasive brain imaging (MEG/EEG) information capacity. Derives technology-independent bounds from Planck constant, neural metabolism, and geometry. Use when analyzing limits of magnetoencephalography, quantum sensors for neuroscience, brain imaging spatio-temporal tradeoffs, or metabolic information capacity bounds. Based on arXiv 2511.06401.Votes: 0GitHub stars: 3
- Quantum Multitime MemoryMultitime memory methodology beyond quantum regression theorem for sequential measurement statistics. Use when analyzing non-Markovian quantum processes, multi-time correlation functions, quantum memory effects, or sequential quantum measurement scenarios where standard regression theorem fails.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 HybridHybrid classical-quantum neural network development skill. Provides workflows for transfer learning, quantum error mitigation, and noise-resistant quantum neural networks. Use when working with quantum machine learning (QML), variational quantum circuits (VQC), quantum-classical hybrid architectures, or implementing quantum neural networks on NISQ devices. Supports PennyLane, Qiskit, and other quantum ML frameworks.Votes: 0GitHub stars: 3
- Quantum Neural IntersectionQuantum theory and neural network intersection research skill. Analyzes cross-disciplinary patterns between quantum mechanics and neural architectures. Activation: quantum neural, quantum machine learning, quantum field theory neural, 神经量子, 量子神经网络.Votes: 0GitHub stars: 3
- Quantum Neural Measurement DynamicsQuantum neural network measurement dynamics and critical phenomena — Born-rule statistics, Leggett-Garg tests, and dynamical quantum phase transitions in neural systemsVotes: 0GitHub stars: 3
- Quantum Neural Network CrossingQuantum-Neural Network Cross-Domain Research skill - bridges quantum computing with neural network architectures for hybrid model design and analysis. Activation: quantum neural network, 量子神经网络, quantum deep learning, hybrid quantum-classical, quantum ML, variational quantum circuits.Votes: 0GitHub stars: 3
- Quantum Neural Network Data LoadingEfficient data loading paradigm for Quantum Neural Networks using Shot-Based Quantum Encoding (SBQE). Distribute shots according to data-dependent classical distribution. Use when implementing QNN, quantum data loading, or quantum machine learning.Votes: 0GitHub stars: 3
- Quantum Neural Network DesignerDesign and optimize quantum neural network architectures based on Lie algebra truncation and parameterized quantum circuit theory. Use when working with quantum machine learning tasks: (1) Designing QNN architectures for classification/regression, (2) Analyzing trainability and barren plateaus, (3) Optimizing quantum circuit expressivity, (4) Evaluating noise robustness of QNNs. Keywords: quantum neural network, QNN design, quantum circuit, parameterized quantum circuit, LieTrunc-QNN, barren ...Votes: 0GitHub stars: 3
- Quantum Neural States Grand CanonicalNeural network quantum state architecture for grand canonical ensemble simulations. Use when representing symmetric bosonic wavefunctions in Fock space, studying quantum many-body systems with variable particle number, computing one-body reduced density matrices, or estimating condensate fractions and radial density profiles from first principles.Votes: 0GitHub stars: 3
- Quantum Neural TopologyResearch skill for quantum neural networks and topological field theory - combines quantum computing, neural architecture, and topological mathematics. Use when: (1) Analyzing quantum ML patterns, (2) Synthesizing quantum-neural research, (3) Exploring topological neural architectures, (4) Identifying quantum stability mechanisms, (5) Creating quantum-classical hybrid frameworks. Keywords: quantum neural, quantum topology, QML research, topological neural, quantum architecture, 量子神经网络, 拓扑神经网络.Votes: 0GitHub stars: 3