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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Quantum Neuromorphic ArchitecturesDesign patterns for quantum-neuromorphic computing including QHDC mappings, three-layer quantum brain CQEC analysis, LMG Hamiltonian phase transitions, and photonic quantum memristors.Votes: 0GitHub stars: 3
- Quantum Neuromorphic PatternsQuantum neuromorphic computing patterns — combining quantum computing with brain-inspired neural architectures. Covers quantum brain modeling, quantum reservoir computing for neural dynamics, brain-inspired quantum neural architectures, spiking-phase quantum encoding, and quantum-inspired cognitive models. Use when designing quantum systems for neuroscience applications, brain-inspired quantum algorithms, or quantum-enhanced neural network architectures. Trigger: quantum neuromorphic, 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, quantum spiking neural networks, quantum brain-inspired computing, covariant quantum error correction in biological systems, quantum photonic neural networks, and quantum cognitive modeling. Use when searching quantum neuroscience papers, analyzing quantum-ML architectures, designing quantum neuromorphic systems, or studying biological quantum coherence.Votes: 0GitHub stars: 3
- Quantum Neuroscience PatternsResearch methodology bridging quantum computing and neuroscience. Covers quantum hyperdimensional computing (QHDC), quantum generative models for neuronal data, quantum-enhanced EEG encoding (QEEGNet), Leggett-Garg tests in neural dynamics, and quantum neuromorphic architectures. Use when: researching quantum brain models, quantum neural networks, quantum-EEG hybrid systems, quantum generative models for biological data, neuromorphic quantum architectures, or testing quantum effects in neural...Votes: 0GitHub stars: 3
- Quantum Optical NeuronQuantum optical neuron methodology — camera-free image classification via Hong-Ou-Mandel interference of spatially programmable single photons. Two-photon coincidences directly report overlap between input image mode and learned template. Use when building neuromorphic quantum photonic processors, photon-starved imaging systems, or quantum-classical hybrid inference pipelines.Votes: 0GitHub stars: 3
- Quantum Photonic Neural NetworksTime-bin-encoded Quantum Photonic Neural Networks (QPNN) architecture. Reconfigurable nonlinear photonic circuits inspired by the brain, trained to process quantum information. Time encoding requires constant number of photonic elements regardless of network size/depth. Use when: quantum photonic circuits, time-encoded QNN, photonic neural networks, quantum dot nonlinearities, Bell-state analysis, Kerr nonlinearity.Votes: 0GitHub stars: 3
- Quantum Photonic Reservoir ComputingEfficient classical training of model-free quantum photonic reservoirs. Implements quantum extreme learning machines with classical-light training and quantum inference. Activation: quantum photonic reservoir, quantum ELM, classical training quantum reservoirVotes: 0GitHub stars: 3
- Quantum Reservoir Computing FinanceQuantum Reservoir Computing (QRC) methodology for financial time series forecasting. Uses transverse-field Ising Hamiltonian as reservoir with distinct input and memory qubits to capture temporal dependencies. Benchmarked against econometric models and ML algorithms, consistently outperforms benchmarks. Use wrapper-based forward selection for feature selection and Shapley values for interpretability. Applicable to volatility forecasting, stock prediction, and quantitative finance. Also useful...Votes: 0GitHub stars: 3
- Quantum Reservoir Computing Risk BoundsRademacher complexity-based generalization error bounds for quantum reservoir computing (QRC). Covers parameter-dependent bounds for quantum reservoir classes, qubit-scaling analysis, and polynomial readout function risk bounds. Use when: quantum reservoir computing generalization, QRC risk analysis, reservoir capacity bounds, or quantum ML theoretical guarantees.Votes: 0GitHub stars: 3
- Quantum Reservoir FinanceQuantum Reservoir Computing (QRC) methodology for financial time-series forecasting. Uses small-scale quantum systems (≤6 qubits) as nonlinear reservoirs for stock trend classification with >86% accuracy. Platform-agnostic across superconducting circuits and trapped ions. Use when: (1) stock movement prediction, (2) financial time-series forecasting with quantum computing, (3) small-scale quantum advantage demonstration, (4) quantum reservoir computing, (5) quantum-invested market analysis.Votes: 0GitHub stars: 3
- Quantum Reservoir Forecasting Resource EfficientResource-efficient Quantum Reservoir Computing framework for time-series forecasting. Combines fixed quantum reservoir transformation with post-training quantized classical readout for deployment on edge/limited-memory devices.Votes: 0GitHub stars: 3
- Quantum Reservoir MemoryControllable quantum memory capacity methodology for quantum reservoir computing using tunable partial-SWAP gates. Unifies feedback-based and recurrent QRC architectures through partial-SWAP interpolation parameter, enabling controllable trade-off between memory capacity and processing speed. Use when: (1) designing quantum reservoir computing systems, (2) tuning quantum memory capacity, (3) choosing between feedback and recurrent QRC architectures, (4) implementing temporal quantum machine l...Votes: 0GitHub stars: 3
- Quantum Reservoir Stock ForecastingQuantum Reservoir Computing (QRC) methodology for financial time-series forecasting using small-scale quantum systems. Use when: building quantum-enhanced stock prediction models, applying reservoir computing to finance, designing near-term quantum ML for temporal data, or forecasting trading volumes/stock trends. Activation: quantum reservoir computing, QRC stock prediction, quantum time-series forecasting, quantum stock movement, reservoir computing finance.Votes: 0GitHub stars: 3
- Quantum Reservoir Time Series ForecastingQuantum Reservoir Computing (QRC) methodology for financial time series forecasting. Exploits quantum dynamics for temporal pattern recognition, achieving prediction accuracy improvements while maintaining quantum coherence. Activation: quantum reservoir computing, time series forecasting, financial prediction, QRC, quantum temporal dynamics, reservoir dynamics, quantum echo state.Votes: 0GitHub stars: 3
- Quantum Sensor ReliabilityImprove quantum sensor network reliability through RL-optimized dynamical decoupling (DD) pulse sequences. Use when mitigating environmental decoherence in quantum sensors, optimizing DD pulse sequences, designing hybrid quantum-classical sensing pipelines, or addressing noise-aware control in quantum sensor networks. Applies to quantum sensing, quantum-classical HPC integration, and noise-adaptive quantum control systems.Votes: 0GitHub stars: 3
- Quantum Spiking Network FusionQuantum-Spiking Neural Network Fusion methodology — integrating quantum computing with neuromorphic spiking architectures for hybrid intelligent systems with quantum-enhanced temporal processing.Votes: 0GitHub stars: 3
- Quantum Synchronization Dynamics FrameworkUnified quantum synchronization framework combining Fock state synchronization (phase-locking non-classical states with negative Wigner function, Arnold tongue regime, phase slip rate extraction) and limit cycle desynchronization (quantum phase slip proliferation degrading phase locking, Keldysh path integral, non-Markovian effects). Applies to quantum control, quantum optics, bosonic systems, quantum information processing. Activation: quantum synchronization, Fock state, phase locking, Arno...Votes: 0GitHub stars: 3
- Quantum Timeseries Transformer FmriQuantum Time-series Transformer (QTS) methodology for resting-state fMRI analysis using Linear Combination of Unitaries (LCU) and Quantum Singular Value Transformation (QSVT). Achieves polylogarithmic complexity with superior small-sample performance. Activation: quantum transformer fMRI, quantum time-series, QTS, quantum fMRI analysis, resting-state quantum.Votes: 0GitHub stars: 3
- Quantum Tunnelling Oscillators CognitionQuantum-tunnelling oscillator model as universal dynamical engine for quantum cognition — models optical illusion perception and group decision making as quantum-mechanical agents with context-dependent state transitions, networked into quantum-cognitive neural systems. Activation: quantum cognition, quantum tunnelling oscillators, optical illusion perception, group decision making, quantum-cognitive neural systems, context-dependent transitions, 量子认知振荡器, 量子隧穿, 群体决策Votes: 0GitHub stars: 3
- Quantum Vector Hopfield NetworkQuantum vector Hopfield network methodology where quantum fluctuations stabilize stored patterns via quantum order-by-disorder mechanism. Patterns formed by quantum vector spin orientations. Both critical retrieval temperature and pattern overlap enhanced vs classical. Use when: quantum associative memory, quantum Hopfield networks, quantum order-by-disorder, quantum-enhanced memory, quantum spin networks, pattern stabilization. Activation: quantum vector hopfield, quantum associative memory,...Votes: 0GitHub stars: 3
- Quasilinear Equivalence Checking Detector ErrorQuasilinear Equivalence Checking for Detector Error Models (arXiv: 2606.14677v1). A Detector Error Model (DEM) is a structured representation of error mechanisms ...Votes: 0GitHub stars: 3
- Qubridge Fidelity DecompositionPipeline analysis tool for decomposing quantum computation fidelity contributions by decision layer. Use when analyzing how different compilation decisions (qubit selection, gate scheduling, pulse shaping, error detection) contribute to final circuit output quality, or when optimizing quantum circuit execution under calibrated noise models. Based on arXiv:2605.11529 (QuBridge). Activation: quantum fidelity decomposition, compilation pipeline analysis, qubridge, fidelity contribution, quantum ...Votes: 0GitHub stars: 3
- Quiet Edge Centric Brain SynchronizationQUIET: Edge-centric framework for targeted brain network synchronization. Integrates structural controllability with functional connectivity to identify energy-efficient synchronization pathways. Identifies 'quiet highways' - edges that are structurally influential but functionally underutilized. Validated on HCP data showing salience network control energy correlates with fluid intelligence. Applied to dexmedetomidine sedation showing frontoparietal and default-mode networks require largest ...Votes: 0GitHub stars: 3
- Quotient Homology Neural RepresentationQuotient homology theory framework for neural network representations - uses algebraic topology to intrinsically compute Betti numbers without external metrics via overlap decomposition. Activation: homology, topology, Betti numbers, neural representation, algebraic topology, quotient space, piecewise linear, ReLU networks, manifold decomposition.Votes: 0GitHub stars: 3
- Qutrit Entropy Neural EstimationVon Neumann entropy estimation in multi-qutrit systems using VQA and classical CNN approaches. CNN achieves accurate estimation using only 12.5% of full tomography measurements. Use when estimating quantum state entropy, benchmarking VQA ansatzes, or applying neural networks to quantum state characterization.Votes: 0GitHub stars: 3
- Random Network Neural DimensionalityRandom neural network methodology for matching observed neural population dimensionality - Dynamical Mean-Field Theory approach for quantitatively comparing random network models to neural recordings.Votes: 0GitHub stars: 3
- Random Riemann Zeta SpectrumRandom Riemann Zeta Function integral means spectrum methodology — connects random vertical shifts of zeta-function to Kraetzer's universal integral means spectrum conjecture via Gaussian multiplicative chaos (GMC). Use for: analytic number theory, random zeta functions, GMC, conformal mapping, multifractal analysis. arXiv: 2603.26507.Votes: 0GitHub stars: 3
- Rats Register Attention TransformersRATS methodology for analyzing emergent part-based representations in Register Attention Transformers. Reveals how attention patches develop specialized, reusable structural components through register-based communication. Use when: mechanistic interpretability, transformer internal analysis, register attention, emergent representations, attention patch specialization.Votes: 0GitHub stars: 3
- Realm Lfp Retrospective DecodingREALM methodology for LFP-based behavior decoding using retrospective distillation. Use when: building causal LFP decoding models, offline-to-online distillation for neural signals, Mamba-based neural sequence modeling, BCI decoding without spike signals, reducing bandwidth/power in implantable BCIs, behavior decoding from local field potentials. Activation: LFP decoding, REALM, retrospective distillation, causal neural decoding, wireless BCI, Mamba neural model, spike-free decoding.Votes: 0GitHub stars: 3
- Realtime Snn Object Detection EdgeReal-time object detection with Spiking Neural Networks on edge neuromorphic hardware. Covers SNN architecture design, ANN-to-SNN distillation training, and deployment on Intel Loihi 2. Trigger words: SNN object detection, neuromorphic object detection, Loihi 2 deployment, event-based detection, edge SNN detection, SNN distillation training.Votes: 0GitHub stars: 3
- Recap Local Hebbian Prototype Learning As A Selforganizing Readout For Reservoir Dynamics**arXiv ID:** 2603.06639 **Authors:** Heng Zhang **Published:** 2026-02-25T08:28:35Z **Abstract:** Robust perception in brains is often attributed to high-dimensional population activity together with local plasticity mechanisms that reinforce recurring structure. In contrast, most modern image recognition systems are trained by error backpropagation and end-to-end gradient optimization, which are not naturally aligned with local computation and local plasticity. We introduce RECAP (Reservoir...Votes: 0GitHub stars: 3
- Relevanceguided Unsupervised Discovery Of Abilities With Qualitydiversity Algorithms**arXiv ID:** 2204.09828 **Authors:** Luca Grillotti, Antoine Cully **Published:** 2022-04-21T00:29:38Z **Abstract:** Quality-Diversity algorithms provide efficient mechanisms to generate large collections of diverse and high-performing solutions, which have shown to be instrumental for solving downstream tasks. However, most of those algorithms rely on a behavioural descriptor to characterise the diversity that is hand-coded, hence requiring prior knowledge about the considered tasks. In thi...Votes: 0GitHub stars: 3
- Reshaping Neural Representation Presynaptic PlasticityAssociative presynaptic short-term plasticity via information-theoretic learning rules maximizing stimulus information under resource constraintsVotes: 0GitHub stars: 3
- Rethinking Functional Brain Connectome Analysis Do Graph Deep Learning Models Help**arXiv ID:** 2501.17207 **Authors:** Keqi Han, Yao Su, Lifang He, Liang Zhan, Sergey Plis, Vince Calhoun, Carl Yang **Published:** 2025-01-28T07:24:16Z **Abstract:** Graph deep learning models, a class of AI-driven approaches employing a message aggregation mechanism, have gained popularity for analyzing the functional brain connectome in neuroimaging. However, their actual effectiveness remains unclear. In this study, we re-examine graph deep learning versus classical machine learning model...Votes: 0GitHub stars: 3
- Rethinking Skip Connection Model As A Learnable Markov Chain**arXiv ID:** 2209.15278 **Authors:** Dengsheng Chen, Jie Hu, Wenwen Qiang, Xiaoming Wei, Enhua Wu **Published:** 2022-09-30T07:31:49Z **Abstract:** Over past few years afterward the birth of ResNet, skip connection has become the defacto standard for the design of modern architectures due to its widespread adoption, easy optimization and proven performance. Prior work has explained the effectiveness of the skip connection mechanism from different perspectives. In this work, we deep dive into...Votes: 0GitHub stars: 3
- Retina Gap Junction Defense基于视网膜间隙连接灵感的EEG-BCI系统对抗防御方法。使用脉冲神经网络实现生物噪声注入，提升脑机接口系统对对抗攻击的鲁棒性。适用于BCI安全、对抗防御、EEG分类、脉冲神经网络。触发词：retina gap junction, adversarial defense, BCI security, EEG robustness, 间隙连接防御, 对抗鲁棒性Votes: 0GitHub stars: 3
- Retinomorphic Optical Spiking NeuronHodgkin-Huxley-based optical spiking neuron (OSHN) methodology for energy-efficient retinomorphic vision processing and camouflaged object detection. Uses 2D anti-ambipolar phototransistor in subthreshold regime, emulates retinal center-surround receptive fields, achieves sub-picojoule spike energy.Votes: 0GitHub stars: 3
- Retrieval Brain Decoding AlignmentLinear contrastive decoders outperform complex nonlinear models for fMRI-based brain decoding. Key insight: fMRI averaging linearizes representations, so training objective (contrastive alignment) matters more than architectural complexity. Validated across vision, language, audio. Activation: brain decoding, fMRI, contrastive learning, linear decoder, alignment, foundation models.Votes: 0GitHub stars: 3
- Reve Eeg FoundationREVE (Representation for EEG with Versatile Embeddings) - EEG foundation model trained on 60,000 hours from 25,000 subjects with novel 4D positional encoding for arbitrary electrode configurations. Achieves SOTA on 10 downstream tasks. Activation triggers: EEG foundation model, REVE, versatile embeddings, 4D positional encoding, cross-dataset EEG, brain-computer interface.Votes: 0GitHub stars: 3
- Reverse Engineering Brain Control NodesReverse Engineering Brain Control NodesVotes: 0GitHub stars: 3
- Reward Valuation Vlm Anhedonia CausalMechanistic framework identifying reward-anticipatory units in Vision-Language Models (VLMs) that parallel Nucleus Accumbens (NAc) function. Causal perturbation of NAc-selective units induces anhedonia-like behavioral shifts toward low-effort, low-reward options. Validates alignment between AI reward circuits and human dopaminergic reward system.Votes: 0GitHub stars: 3
- Rhythm Snn Temporal ProcessingNeural oscillation-inspired SNN architecture for enhanced temporal processing and noise robustness. Based on Nature Communications 2025 Rhythm-SNN paper.Votes: 0GitHub stars: 3
- Riemannian Self Attention Eeg DecodingBures-Wasserstein metric-based Riemannian self-attention network for robust EEG decoding in BCI. Overcomes AIM quadratic dependency and ill-conditioning issues. Activation: riemannian self attention EEG, Bures-Wasserstein EEG decoding, GBWAtt, SPD learning BCI, Riemannian manifold EEGVotes: 0GitHub stars: 3
- Rim Reasoning In MemoryLatent reasoning method that replaces autoregressive generation with memory blocks - working memory capacity for compute-efficient reasoningVotes: 0GitHub stars: 3
- Rl Closed Loop Eeg TmsReinforcement learning-based closed-loop EEG-TMS system for personalized brain stimulation. Uses RL to identify individual-specific brain state markers for optimized neurostimulation.Votes: 0GitHub stars: 3
- Rllogo Deep Reinforcement Learning Localization For Logo Recognition**arXiv ID:** 2312.16792 **Authors:** Masato Fujitake **Published:** 2023-12-28T02:44:28Z **Abstract:** This paper proposes a novel logo image recognition approach incorporating a localization technique based on reinforcement learning. Logo recognition is an image classification task identifying a brand in an image. As the size and position of a logo vary widely from image to image, it is necessary to determine its position for accurate recognition. However, because there is no annotation for...Votes: 0GitHub stars: 3
- Rnn Structural Design Computational AbilityPaper analysis: Identifying structural design principles shaping computational abilities of recurrent neural networks. Demonstrates that local 2- and 3-cycles in connectivity strongly enhance computational ability of RNNs, and that adding sparse biologically-inspired interneurons dramatically increases capacity. Complete catalogs of network-function performance reveal most networks fail at most functions. Source: arXiv:2606.23874 (q-bio.NC, cs.NE), 2026-06-22. Activation keywords: RNN structu...Votes: 0GitHub stars: 3
- Robust Lagrangian And Adversarial Policy Gradient For Robust Constrained Markov Decision Processes**arXiv ID:** 2308.11267 **Authors:** David M. Bossens **Published:** 2023-08-22T08:24:45Z **Abstract:** The robust constrained Markov decision process (RCMDP) is a recent task-modelling framework for reinforcement learning that incorporates behavioural constraints and that provides robustness to errors in the transition dynamics model through the use of an uncertainty set. Simulating RCMDPs requires computing the worst-case dynamics based on value estimates for each state, an approach which ...Votes: 0GitHub stars: 3
- Robustness Of Humans And Machines On Object Recognition With Extreme Image Transformations**arXiv ID:** 2205.05167 **Authors:** Dakarai Crowder, Girik Malik **Published:** 2022-05-09T17:15:54Z **Abstract:** Recent neural network architectures have claimed to explain data from the human visual cortex. Their demonstrated performance is however still limited by the dependence on exploiting low-level features for solving visual tasks. This strategy limits their performance in case of out-of-distribution/adversarial data. Humans, meanwhile learn abstract concepts and are mostly unaffec...Votes: 0GitHub stars: 3