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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Provable Benefits Of Complex Parameterizations For Structured State Space Models**arXiv ID:** 2410.14067 **Authors:** Yuval Ran-Milo, Eden Lumbroso, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen **Published:** 2024-10-17T22:35:50Z **Abstract:** Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most notably diagonal. In contrast to typical neural network modules, whose parameterizations are real, SSMs often use complex parameterizations. ...Votes: 0GitHub stars: 3
- Prr Speculate Reuse Repair Sparse AttentionPredict-Reuse-Repair (PRR) runtime for accelerating dynamic sparse attention in long-context LLM decoding. Speculates attention over predicted KV blocks while selection is in flight, then incrementally repairs missed blocks. Reduces per-token decoding latency up to 40%.Votes: 0GitHub stars: 3
- Psychosis Scaling Critical Regime精神病早期阶段脑动力学临界性scaling偏差研究方法论。结合重整化群(RG)框架与多种scaling分析方法，揭示临界 regime内的动力学重组而非临界性丧失。Votes: 0GitHub stars: 3
- Puda An Ai Native Hardware Harness For Self DrivinPUDA: An AI-Native Hardware Harness for Self-Driving LaboratoriesVotes: 0GitHub stars: 3
- Pwo Trust Region Nqs OptimizationProximal Wavefunction Optimization (PWO) methodology for training Neural Quantum States using trust-region optimization. Clips probability-ratio changes in amplitude and phase channels, scales NQS training to billion-parameter models without explicit matrix inversion.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
- Q Biolat Protein Fitness QuantumQ-BIOLAT: Binary latent protein fitness landscapes for quantum annealing optimization. Maps protein sequences to binary latent spaces via pretrained protein language models, then uses quantum annealing (D-Wave) for fitness landscape exploration and protein engineering.Votes: 0GitHub stars: 3
- Q Photonas Quantum NasQ-PhotoNAS: Hybrid Quantum Neural Architecture Search framework for photonic quantum-classical models using genetic algorithm and learnable phase encodingVotes: 0GitHub stars: 3
- Qadr Distributed Entanglement ReductionQuantum Algorithm for Distributed Reduction of Entanglements (QADR) — hybrid quantum-classical ML framework that decomposes global VQCs into localized sub-circuits within causal light cones. Reduces classical simulation memory from O(2^n) to O(2^d) while mitigating barren plateaus. arXiv:2606.01291Votes: 0GitHub stars: 3
- Qae Mri Anomaly DetectionQuantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI data - angle encoding into quantum states, variational encoder-decoder with trash qubits, achieving 0.95 slice-level ROC-AUC.Votes: 0GitHub stars: 3
- Qaoa Manifold OptimizationRiemannian manifold optimization techniques for enhancing QAOA performance on NISQ devices. Leverages intrinsic geometric structure to address nonconvexity of QAOA objective function and overcome challenges with traditional gradient descent optimizers. Use when optimizing QAOA parameters, dealing with barren plateaus, or improving quantum optimization convergence.Votes: 0GitHub stars: 3
- Qaoa Xy Mixers PortfolioConstraint-preserving QAOA formulation using Dicke state initialization and XY-mixer Hamiltonian for direct indexing portfolio optimization. Use when: implementing QAOA with hard cardinality constraints; designing constraint-preserving quantum ansatzes; mitigating barren plateaus via Trotterized initialization; comparing quantum vs classical portfolio optimization (SA, HRP); Direct Indexing with ESG constraints. Keywords: qaoa, xy-mixer, dicke state, portfolio optimization, constraint-preserv...Votes: 0GitHub stars: 3
- Qaoa Zne PortfolioQAOA + Zero Noise Extrapolation (ZNE) workflow for multi-objective portfolio optimization on real IBM Quantum hardware. Demonstrates QAOA with error mitigation outperforming classical greedy baselines on 88-variable problems with carbon sequestration, biodiversity, and social impact objectives. Use when: (1) running QAOA on real quantum hardware, (2) applying ZNE for error mitigation, (3) multi-objective portfolio optimization, (4) ESG/green finance quantum applications, (5) NISQ-era quantum ...Votes: 0GitHub stars: 3
- Qbalance Quantum Workflow Optimization多目标量子工作流优化方法论。系统化选择 NISQ 设备上的编译策略、噪声抑制和误差缓解方案。基于 QBalance 框架，涵盖加权目标函数、非支配选择规则、生存乘积误差代理、贝叶斯候选排序和分布诊断。Activation: qbalance, quantum workflow, quantum compilation optimization, NISQ error mitigation, quantum noise suppression, multi-objective quantum strategy.Votes: 0GitHub stars: 3
- Qdiffusion Ts Quantum Generative DiffusionQDiffusion-TS - First quantum generative diffusion model for time series synthesis with real quantum hardware validation on IQM processorVotes: 0GitHub stars: 3
- Qfi Decoherence Monte CarloQuantum Fisher Information estimation under decoherence via MCMC sampling — maps QFI lower bounds onto classical expectation values over wave function amplitude distributions. Enables QFI estimation for system sizes beyond exact diagonalization. Use when: analyzing metrological content of quantum states under noise, computing QFI bounds for Jastrow-Gutzwiller wave functions, evaluating quantum sensing robustness to dephasing/amplitude damping/depolarizing, or studying entanglement scaling in ...Votes: 0GitHub stars: 3
- Qfi Stabilizer FrameworkQuantum Fisher Information framework for stabilizer codes — constructing nonlocal observables with extensive QFI density for quantum metrology. Maps stabilizer generators to dual Ising spins whose correlators equal string order parameters, converting hidden nonlocal order into metrologically accessible observables. Use when analyzing quantum metrology in stabilizer systems, computing QFI bounds, studying string order parameters, evaluating monitored cluster codes or toric code for sensing, or...Votes: 0GitHub stars: 3
- Qkan Quantum Kolmogorov ArnoldQKAN (Quantum Kolmogorov-Arnold Networks) methodology for quantum machine learning. Implements quantum neural networks using block-encodings and quantum singular value transformation. Use when working with quantum ML models, quantum function approximation, or multivariate state preparation. Activation: QKAN, quantum Kolmogorov Arnold, quantum neural networks, quantum ML.Votes: 0GitHub stars: 3
- Qkd Efficiency Mismatch CountermeasurePractical countermeasure methodology against QKD attacks exploiting detection efficiency mismatch, including time-shift attacks. Uses temporal filtering and detector calibration to ensure uniform detection probability across all degrees of freedom, preventing eavesdropper from gaining information without detection. arXiv:2605.22580Votes: 0GitHub stars: 3
- Qldpc Breakeven EvaluationFramework for evaluating quantum LDPC codes at breakeven point from arXiv:2606.06455. qLDPC codes achieve higher encoding rates than surface codes with demonstrated breakeven on real hardware.Votes: 0GitHub stars: 3
- Qlif Cast Weather ForecastingQLIF-CAST: Quantum Leaky-Integrate-and-Fire methodology for time-series regression (weather forecasting). Hybrid quantum-classical recurrent architecture using single-qubit superpositions for neuron states. Demonstrates 15.4% MSE reduction over classical LIF and 94% faster convergence vs QLSTM/QNN.Votes: 0GitHub stars: 3
- Qlustering Quantum ClusteringUnsupervised clustering via steady-state quantum transport in open quantum networks (GKSL master equation). Encodes data as input states and infers cluster assignments from terminal current observables - no full state tomography required. Use when: quantum clustering, GKSL transport, analog quantum ML, open quantum network clustering, Qlustering algorithm, steady-state quantum transport clustering, tomography-free quantum learning, quantum unsupervised learning, algorithm-hardware co-design c...Votes: 0GitHub stars: 3
- Qml Adversarial Robustness SokSoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness. Comprehensive systematization of adversarial attacks on QML systems across black-box, gray-box, and white-box threat models with empirical evaluation of poisoning, backdoor, and evasion attacks. Reveals accuracy-robustness trade-offs between encoding schemes and proposes threat-aware, noise-resilient framework for secure QML deployment. Trigger: QML security, quantum adversarial robustness, quantum classifier att...Votes: 0GitHub stars: 3
- Qml Equilibrium Propagation MedicalQuantum Machine Learning with Equilibrium Propagation for medical image analysis. Energy-based training without backpropagation using Variational Quantum Circuits (VQCs) for resource-constrained quantum hardware. Use when: analyzing blood cells, leukemia detection, medical imaging with QML, energy-based quantum training, backprop-free quantum networks, or evaluating QML feasibility on NISQ devices.Votes: 0GitHub stars: 3
- Qml Expressivity SeparationQuantum Machine Learning expressivity separation methodology. Based on Anschuetz & Gao (Quantum 10, 1976, 2026). Provides framework for constructing efficiently trainable QNNs with provable polynomial memory separations over classical neural networks. Use when: (1) designing QNN architectures with provable quantum advantage, (2) analyzing expressivity vs trainability trade-offs, (3) implementing quantum contextuality as computational resource, (4) comparing quantum vs classical sequence model...Votes: 0GitHub stars: 3
- Qml Expressivity Trainability ParadoxDynamical Lie Algebra framework for understanding and navigating the expressivity-trainability paradox in Quantum Machine Learning. Shows unstructured QML suffers quantum underfitting from barren plateaus. Symmetry-preserving structural regularization guarantees scalable gradient-rich landscapes. Trainability-by-Design approach.Votes: 0GitHub stars: 3
- Qml Framework Agnostic DesignDesign framework-agnostic quantum machine learning (QML) systems using the Model-Agnostic Learning System (MALS) paradigm. Extracts QML models from any framework (PennyLane, Qiskit, TensorFlow Quantum, etc.) into portable representations with auto-validation and cross-framework compatibility testing.Votes: 0GitHub stars: 3
- Qml Model TestingQuantum Machine Learning model testing and robustness analysis methodology. Covers mutation testing for QNN circuits, accuracy/robustness evaluation of Variational Quantum Circuits (VQCs), and practical considerations for deploying QML models on NISQ-era quantum hardware. Use when: (1) testing quantum neural network implementations for correctness, (2) evaluating QML model robustness against circuit faults and noise, (3) designing test suites for parametrized quantum circuits, (4) analyzing V...Votes: 0GitHub stars: 3
- Qml Multidimensional BenchmarkingMultidimensional benchmarking framework for comparing Quantum vs Classical ML models across accuracy, runtime, parameter count, and memory. Provides practical operating points (qubit count, sample size) that balance accuracy vs cost. Use when: deciding whether to use QML vs classical ML, benchmarking quantum advantage, resource allocation for ML pipelines, parameter/memory efficiency analysis.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
- Qmt Hybrid Qnn Training StabilityQuantum Measurement Temperature (QMT) methodology for stabilizing hybrid QNN training. Addresses measurement-induced logit contraction in variational quantum classifiers for protein and medical image classification.Votes: 0GitHub stars: 3
- Qnn Option Pricing NisqQuantum Neural Network (QNN) approach for option pricing on NISQ hardware - methodology for implementing quantum derivative pricing across multiple quantum processors, benchmarking cross-platform performance, and approximating Black-Scholes-Merton pricing functions using QNNs. arXiv: 2604.19832Votes: 0GitHub stars: 3
- Qnn Survey Design PatternsQuantum Neural Network (QNN) design patterns and architecture selection guide — comprehensive survey methodology for selecting, designing, and evaluating QNN architectures based on task requirements. Covers fully connected QNNs, quantum CNNs, equivariant QNNs, quantum Hopfield networks, quantum Boltzmann machines, quantum reservoir computing, and composite networks. Activation: QNN survey, quantum neural network design, QNN architecture selection, quantum machine learning survey, 量子神经网络综述Votes: 0GitHub stars: 3
- Qrc Symmetry ExploitationSymmetry exploitation methodology for quantum reservoir computing - observable-orbit completion aligning encoding, dynamics, measurement, and readout for symmetric tasksVotes: 0GitHub stars: 3
- Qredumis Quantum Portfolio PipelineqReduMIS recursive hybrid quantum-classical pipeline for portfolio diversification using QAOA frozen-node identification on asset correlation graphs. Validated on Quantinuum 98-qubit trapped-ion Helios system with real market data up to 225 assets.Votes: 0GitHub stars: 3
- Qsp Control QuantumQuantum Signal Processing (QSP) framework for analytical quantum control of qubit-oscillator systems. Use when designing quantum control protocols, mitigating cross-Kerr interactions, constructing Fock-state-selective operators, or mapping control problems to QSP form. Triggers: QSP control, quantum signal processing control, qubit-oscillator control, Fock state manipulation, cross-Kerr mitigation, analytical quantum control, Jaynes-Cummings QSP.Votes: 0GitHub stars: 3
- Qtaml Quantum Tunneling MlQuantum Tunneling-Aware Machine Learning (QTAML) — derives deployment-time weight-error distribution from WKB approximation, provides closed-form Tunneling-Aware Compensation (TAC) algorithm that reaches 95% clean accuracy with 3.4-33.6x less ECC overhead. Bridges semiconductor physics with noise-aware ML. arXiv:2606.00741Votes: 0GitHub stars: 3
- Qtnvqc An Endtoend Learning Framework For Quantum Neural Networks**arXiv ID:** 2110.03861 **Authors:** Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen **Published:** 2021-10-06T14:44:51Z **Abstract:** The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks. To bridge the gap, this work proposes an end-to-end learning framework named QTN-VQC, by introducing a trainable quantum tensor network (QTN) for quantum embedding on a variational quantum circuit (VQC). The ar...Votes: 0GitHub stars: 3
- Quadratically Constrained Quadratic Programming For Classification Using Particle Swarms And Applications**arXiv ID:** 1407.6315 **Authors:** Deepak Kumar, A G Ramakrishnan **Published:** 2014-07-23T18:04:23Z **Abstract:** Particle swarm optimization is used in several combinatorial optimization problems. In this work, particle swarms are used to solve quadratic programming problems with quadratic constraints. The approach of particle swarms is an example for interior point methods in optimization as an iterative technique. This approach is novel and deals with classification problems without th...Votes: 0GitHub stars: 3
- Quality Factor Oscillator Information CarriersQuality-factor screen for biological oscillator carriers.Votes: 0GitHub stars: 3
- Quanforge Qnn TestingMutation testing framework for Quantum Neural Networks (QNNs) based on the QuanForge methodology (arXiv:2604.20706). Use this skill when testing QNN robustness, analyzing quantum circuit vulnerabilities, performing mutation testing on quantum ML models, localizing weak regions in quantum circuits, or comparing QNN test suites. Also triggered by keywords: quantum testing, QNN testing, mutation testing, 量子测试, 量子神经网络测试.Votes: 0GitHub stars: 3
- Quantized Time Quantum WalksQuantized time statistics methodology for quantum walks under weak rank-K measurements using topological winding numbersVotes: 0GitHub stars: 3
- Quantum 2x2 Game Mathematical FrameworkRigorous mathematical framework for quantum game theory applied to static 2x2 games. Proves existence of Nash equilibria for continuous quantum mixed strategies via fixed-point argument, generalizing classical Nash theorem to quantum case. Extends classical concepts to quantum setting with arbitrary unitary operations (pure strategies) and probability measures over SU(2) (mixed strategies). Use when: quantum game theory foundations, 2x2 quantum games, quantum Nash equilibrium proof, EWL proto...Votes: 0GitHub stars: 3
- Quantum 6g Edge NetworkQuantum Machine Learning methodology for 6G edge network adaptive communication and model aggregation in V2X systems. Combines quantum ML with edge computing for efficient vehicular communication, model collaboration, and generalization. Use when: (1) designing 6G quantum-enhanced networks, (2) V2X communication optimization, (3) edge AI model aggregation, (4) quantum ML for communication systems, (5) adaptive quantum edge networks.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
- Quantum Adversarial DefenseQuantum adversarial defense methodology using quantum autoencoders for protecting quantum classifiers against adversarial perturbations. Covers quantum autoencoder purification, adversarial training-free defense frameworks, confidence metrics for adversarial sample detection, and evaluation of variational quantum classifiers under attack. Use when defending QML models, analyzing quantum adversarial robustness, implementing purification-based defenses, or studying adversarial attacks on variat...Votes: 0GitHub stars: 3
- Quantum Ai PatternsReusable research patterns at the intersection of quantum computing and artificial intelligence. Use when analyzing quantum machine learning papers, designing hybrid quantum-classical systems, or extracting architectural patterns from quantum-AI research. Covers QNN design, distributed quantum computing, AI-assisted error correction, and continuous-time quantum models. Triggers: quantum machine learning, QNN, quantum neural network, hybrid quantum-classical, quantum AI patterns, distributed q...Votes: 0GitHub stars: 3
- Quantum Ai ReliabilityQuantum-enhanced AI reliability patterns from cutting-edge research. Covers certified training of quantum neural networks, quantum interval bound propagation (QIBP), genetic algorithm-based HQNN optimization (GAT-QNN), distributed quantum reinforcement learning (MADQRL), and conformal uncertainty quantification for quantum operator learning. Use when working with quantum neural networks, NISQ-era quantum ML, robustness certification for quantum models, distributed quantum computing, or uncert...Votes: 0GitHub stars: 3
- Quantum Algebraic StructuresQuantum algebraic structures methodology - generalizing quantum Fourier transforms to semisimple algebras, orthogonal polynomial theory for quantum signal processing, and Lie-algebraic Krylov dynamics. Covers efficient QFT constructions beyond finite groups, angle-finding via polynomial sequences, and resource-theoretic quantum scoring. Activation: quantum algebra, semisimple algebra QFT, quantum signal processing, orthogonal polynomial quantum, quantum scoring rules, 量子代数结构.Votes: 0GitHub stars: 3
- Quantum Algorithm Framework Designer[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