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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Photonic Variational TrainabilityPre-asymptotic trainability analysis for photonic variational quantum circuits under postselection. Covers barren plateau dynamics in passive linear-optical circuits, Lie algebra dimension scaling, postselection-induced gradient concentration (allow-bunching, collision-free, dual-rail), and design guidance for near-term photonic variational architectures. Use when: analyzing photonic QNN trainability, designing variational photonic circuits, understanding gradient concentration under postsele...Votes: 0GitHub stars: 3
- Physical Nn Nonlinearity Amplification SuppressionPhysical Neural Networks (PNNs) require nonlinearity, signal amplification, and suppression for learning. Shows through simulation that nonlinearity alone is insufficient for meaningful computation in physical computing paradigms. Presents physically plausible circuit designs incorporating these three essential features. Clarifies limitations of linear physical networks and provides design guidance for energy-efficient physical learning architectures. Use when: designing physical neural netwo...Votes: 0GitHub stars: 3
- Physics Aligned Simulation DeformablePhysics-aligned real-to-sim-to-real data engine for deformable object manipulation. SIM1 grounds simulation in physical world through metric-consistent digital twins, elastic dynamics calibration, and diffusion-based trajectory generation. Use for: deformable object manipulation, sim-to-real transfer, physics-based simulation, robotic manipulation, data-efficient policy learning. Activation: SIM1, physics-aligned simulation, deformable manipulation, real-to-sim-to-real, digital twin, elastic ...Votes: 0GitHub stars: 3
- Physics Guided Quantum LearningPhysics-guided and physics-informed quantum machine learning methodologies — embedding physical priors (symmetries, conservation laws, Hamiltonians) into VQCs, QNNs, and quantum classifiers for improved accuracy, trainability, and physical consistencyVotes: 0GitHub stars: 3
- Physics Informed Llm Quantum ControlPhysics-informed LLM framework (VF-QCTRL) for general quantum control synthesis combining symbolic reasoning with optimization. Proposes analytic control ansätze and refines parameters through feedback loops. Use when designing LLM-driven quantum control, physics-informed prompt engineering, analytic pulse synthesis, or training-free control protocol design across quantum systems. Activation: physics-informed LLM, quantum control synthesis, VF-QCTRL, analytic pulse design, symbolic reasoning ...Votes: 0GitHub stars: 3
- Physics Informed Quantum Error AttributionNeuro-fuzzy framework for quantum error attribution using physics-informed machine learning. Combines ANFIS with physics-grounded features (Bhattacharyya Veto, Data Processing Inequality) to distinguish software bugs from hardware noise in quantum processors. Validated on 156-qubit IBM Heron r2.Votes: 0GitHub stars: 3
- Pinn Quantum Pulse OptimizationUse Physics-Informed Neural Networks (PINNs) for quantum pulse optimization and noise-aware gate fidelity. Specifically for optimizing quantum control pulses in exchange-only spin qubit systems, handling charge noise, and maximizing gate-level fidelity through noise-averaged training. Use when: optimizing quantum pulses, PINN-based quantum control, spin qubit noise mitigation, exchange-only qubits, quantum gate pulse design, charge noise optimization, silicon spin qubits.Votes: 0GitHub stars: 3
- Pinn Small Signal Stability Multi InverterPhysics-informed neural network for small-signal stability analysis in multi-inverter power systems — predicts poles/residues of whole-system impedance across full operating space, identifies oscillation risks and optimal generation distribution.Votes: 0GitHub stars: 3
- Piqc Distributed Quantum ComputingScalable distributed quantum computing architecture using photonic integration of designed molecular quantum nodes. Combines solid-state spin defects in diamond (NV/SiV centers) with nanophotonic waveguide networks for entanglement distribution. Applies systems engineering principles to quantum network design with modular, scalable architectures.Votes: 0GitHub stars: 3
- Plaquette Ftqc Design**Source**: Conchello Vendrell et al., "Plaquette: A hardware-aware design platform for fault-tolerant quantum computers" (arXiv:2607.08767, July 2026)Votes: 0GitHub stars: 3
- Plaquette Ftqc Hardware DesignHardware-aware design platform for fault-tolerant quantum computers (FTQCs). Computes logical performance from device physics using Kraus operators, Hamiltonian-Lindblad dynamics, and quantum channels across four sampler classes.Votes: 0GitHub stars: 3
- Point Group Symmetry QuantumPoint-group symmetry analysis of many-electron wavefunctions on quantum computers. Ancilla-free hybrid method for abelian and non-abelian groups using orbital rotations from representation matrix eigenvectors, tensor-network encoding, and error mitigation for molecular simulation. Application: quantum chemistry, drug discovery, materials science.Votes: 0GitHub stars: 3
- Position Graph Learning Will Lose Relevance Due To Poor Benchmarks**arXiv ID:** 2502.14546 **Authors:** Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Müller, Jan Tönshoff, Antoine Siraudin, Viktor Zaverkin, Michael M. Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, Christopher Morris **Published:** 2025-02-20T13:21:47Z **Abstract:** While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and relevance. Current bench...Votes: 0GitHub stars: 3
- Positive Experience Principle Pep MethodologyMethodology for forecasting conscious choices using the Positive Experience Principle (PEP) derived from the Universal Consciousness Code theory.Votes: 0GitHub stars: 3
- Post Quantum Cryptographic Protocol AnalysisPost-quantum cryptographic analysis of network protocol stacks and message transformations. Evaluates security of cryptographic operations across multiple protocol layers against quantum attacks. Use when: (1) analyzing protocol stack security, (2) evaluating post-quantum cryptography readiness, (3) designing quantum-resistant protocols, (4) auditing cryptographic transformations across layers, (5) migrating systems to post-quantum algorithms.Votes: 0GitHub stars: 3
- Post Quantum Healthcare MigrationPost-Quantum Cryptography (PQC) migration framework for IoT-based healthcare systems. Provides systematic approach for transitioning healthcare infrastructure from classical to post-quantum cryptographic standards (NIST ML-KEM, ML-DSA) while maintaining HIPAA/GDPR compliance. Activation: post-quantum healthcare, PQC migration IoT healthcare, healthcare cryptography upgrade, medical device PQC, HIPAA quantum securityVotes: 0GitHub stars: 3
- Post Quantum Secure PharmacovigilanceNIST-standard PQC migration (ML-KEM + ML-DSA) for pharmacovigilance and healthcare data systems. Use when designing post-quantum security for adverse event reporting, clinical observation systems, or any healthcare pipeline handling sensitive patient data that needs quantum-resistant cryptography.Votes: 0GitHub stars: 3
- Post Training In Time Series Foundation Models A USkill generated from arXiv paper 2607.20002: Post-Training in Time Series Foundation Models: A Unifying FrameworkVotes: 0GitHub stars: 3
- Potassium Current Gain ControlA型钾电流介导的神经元增益控制机制。研究IA作为减法抑制与除法抑制之间的开关，通过动力学系统分析理解神经元如何自调节抑制效果。适用于计算神经科学、神经元建模、增益控制研究。触发词：A型钾电流、增益控制、抑制模式、IA电流、神经元增益、divisive inhibition、subtractive inhibition、gain control、potassium current。Votes: 0GitHub stars: 3
- Potre Cognitive Heterogeneity ReasoningPoTRE (Poly-Topological Reasoning Ensembles) framework for complex reasoning through heterogeneous multi-agent architecture inspired by cognitive heterogeneity.Votes: 0GitHub stars: 3
- Pqc Edge Federated IomtPost-Quantum Cryptography (PQC) integration pattern for Federated Learning (FL) in Internet of Medical Things (IoMT) environments. Covers edge-native orchestration, PQC key establishment, distributed cryptographic processing, and Kubernetes-based scalable frameworks validated on resource-constrained hardware. Activation: post-quantum iomt, pqc federated learning, quantum-resistant healthcare, edge-native cryptography, medical device security, ml-kem ml-dsa healthcare, kubernetes iomt, distrib...Votes: 0GitHub stars: 3
- Pqc Hot FrameworkPQC-HOT framework for post-quantum cryptography implementation in software systems. Analyzes PQC migration through Human, Organisation, and Technology dimensions. Use when implementing post-quantum cryptography, PQC migration, quantum-safe security transitions, or evaluating PQC implementation readiness. Activation: PQC implementation, post-quantum cryptography, quantum-safe migration, PQC-HOT model, quantum security transitionVotes: 0GitHub stars: 3
- Pqc Implementation Hot FrameworkPost-Quantum Cryptography (PQC) implementation framework using Human-Organisation-Technology (HOT) model for software systems. Covers socio-technological constraints, implementation planning, and organizational transition strategies for PQC migration. Use when: PQC implementation planning, post-quantum cryptography migration, quantum-resistant software deployment, HOT framework for crypto, PQC organizational challenges, quantum threat preparation, NIST PQC algorithm integration.Votes: 0GitHub stars: 3
- Prasatul Matrix A Direct Comparison Approach For Analyzing Evolutionary Optimization Algorithms**arXiv ID:** 2212.00671 **Authors:** Anupam Biswas **Published:** 2022-12-01T17:21:44Z **Abstract:** The performance of individual evolutionary optimization algorithms is mostly measured in terms of statistics such as mean, median and standard deviation etc., computed over the best solutions obtained with few trails of the algorithm. To compare the performance of two algorithms, the values of these statistics are compared instead of comparing the solutions directly. This kind of comparison l...Votes: 0GitHub stars: 3
- Predictable Mean Field Chaos RnnKrylov Mean-Field Chaos theory for random recurrent networks — demonstrating that deterministic chaos has latent predictability through Krylov state space decomposition. Extends Hamiltonian chaos concepts to classical dissipative systems.Votes: 0GitHub stars: 3
- Predicting The Time Until A Vehicle Changes The Lane Using Lstmbased Recurrent Neural Networks**arXiv ID:** 2102.01431 **Authors:** Florian Wirthmüller, Marvin Klimke, Julian Schlechtriemen, Jochen Hipp, Manfred Reichert **Published:** 2021-02-02T11:04:22Z **Abstract:** To plan safe and comfortable trajectories for automated vehicles on highways, accurate predictions of traffic situations are needed. So far, a lot of research effort has been spent on detecting lane change maneuvers rather than on estimating the point in time a lane change actually happens. In practice, however, this t...Votes: 0GitHub stars: 3
- Predictive Coding Equilibrium Propagation ImagenetTraining Predictive Coding Networks on ImageNet using Equilibrium Propagation. Biologically plausible training framework for PCNs achieving near-backpropagation performance. Use when: (1) Training predictive coding networks at scale, (2) Implementing biologically plausible learning rules, (3) Scaling equilibrium propagation beyond small tasks, (4) Energy-based model training without backpropagation. Keywords: predictive coding network, PCN, equilibrium propagation, EP, energy-based model, Ima...Votes: 0GitHub stars: 3
- Predictive Maintenance Uncertainty ScenarioScenario-based optimization framework for predictive maintenance scheduling under uncertainty. Integrates calendar-based, usage-based, and condition-monitoring (RUL) information into unified finite-horizon decision framework. Use when: (1) optimizing multi-asset maintenance schedules, (2) dealing with uncertain RUL estimates, (3) comparing expected-cost vs tail-risk maintenance policies, (4) integrating heterogeneous maintenance information sources, (5) scenario-based decision making for asse...Votes: 0GitHub stars: 3
- Predprop Bidirectional Stochastic Optimization With Precision Weighted Predictive Coding**arXiv ID:** 2111.08792 **Authors:** André Ofner, Sebastian Stober **Published:** 2021-11-16T21:43:15Z **Abstract:** We present PredProp, a method for optimization of weights and states in predictive coding networks (PCNs) based on the precision of propagated errors and neural activity. PredProp jointly addresses inference and learning via stochastic gradient descent and adaptively weights parameter updates by approximate curvature. Due to the relation between propagated error covariance and...Votes: 0GitHub stars: 3
- Prelu Yet Another Singlelayer Solution To The Xor Problem**arXiv ID:** 2409.10821 **Authors:** Rafael C. Pinto, Anderson R. Tavares **Published:** 2024-09-17T01:28:40Z **Abstract:** This paper demonstrates that a single-layer neural network using Parametric Rectified Linear Unit (PReLU) activation can solve the XOR problem, a simple fact that has been overlooked so far. We compare this solution to the multi-layer perceptron (MLP) and the Growing Cosine Unit (GCU) activation function and explain why PReLU enables this capability. Our results show th...Votes: 0GitHub stars: 3
- Presorted Tsetlin Machine The Genetic Kmedoid Method**arXiv ID:** 2403.09680 **Authors:** Jordan Morris **Published:** 2024-02-07T15:30:23Z **Abstract:** This paper proposes a machine learning pre-sort stage to traditional supervised learning using Tsetlin Machines. Initially, K data-points are identified from the dataset using an expedited genetic algorithm to solve the maximum dispersion problem. These are then used as the initial placement to run the K-Medoid clustering algorithm. Finally, an expedited genetic algorithm is used to align K i...Votes: 0GitHub stars: 3
- Prime Cohomological Iterative MapsCohomological structure analysis of prime numbers using iterative maps, linking prime irregularities to physical systems including statistical mechanics and quantum mechanics.Votes: 0GitHub stars: 3
- Prime Cohomological MapsCohomological structure analysis methodology for prime numbers — iterative maps predicting prime growth, cohomological equation solutions, and connections between statistical mechanics, quantum mechanics, and number theory. The logarithmic integral function emerges as the solution to the cohomological equation governing prime distribution.Votes: 0GitHub stars: 3
- Prism Probabilistic Intention SwitchingPRISM (Probabilistic Recurrent Intention Switching Model) methodology for multi-intention inverse reinforcement learning. Uses lightweight recurrent networks for intention switching with closed-form EM solution. Activation: 多意图 IRL, intention switching, PRISM, 目标切换, recurrent intention, EM algorithm.Votes: 0GitHub stars: 3
- Pro Long Programmatic Memory Enables Long HorizonSkill generated from arXiv paper 2607.20064: PRO-LONG: Programmatic Memory Enables Long-Horizon ReasoningVotes: 0GitHub stars: 3
- Probabilistic Neural Circuits**arXiv ID:** 2403.06235 **Authors:** Pedro Zuidberg Dos Martires **Published:** 2024-03-10T15:25:49Z **Abstract:** Probabilistic circuits (PCs) have gained prominence in recent years as a versatile framework for discussing probabilistic models that support tractable queries and are yet expressive enough to model complex probability distributions. Nevertheless, tractability comes at a cost: PCs are less expressive than neural networks. In this paper we introduce probabilistic neural circuits ...Votes: 0GitHub stars: 3
- Program As Weights Fuzzy FunctionsProgram-as-Weights (PAW) paradigm for fuzzy function programming. Compile natural-language specifications into compact, locally-executable neural artifacts using parameter-efficient adapters for frozen interpreters. Achieves 32B-level performance at 1/50 inference memory.Votes: 0GitHub stars: 3
- Programmable Dissipation QecProgrammable dissipation methodology for quantum error correction — treating the QEC cycle as a programmable primitive that turns logical noise into a calibrated resource rather than an adversary. One fault-tolerant round induces a logical completely positive trace-preserving (CPTP) map, and decoder/recovery ratio controls the induced dissipation strength. Use when designing open quantum dynamics simulations, fault-tolerant architectures that leverage dissipation, or programmable quantum chan...Votes: 0GitHub stars: 3
- Progressive CrystallizationProgressive crystallization methodology for turning AI agent exploration into deterministic, lower-cost workflows. Three-stage execution taxonomy with evidence-based promotion/demotion mechanism. (arXiv: 2607.07052)Votes: 0GitHub stars: 3
- Progressive Disclosure You Need Long Context AgentsSkill derived from arXiv:2607.17598 - Is Progressive Disclosure All You Need for Long-Context Agents?Votes: 0GitHub stars: 3
- Progressive Swapping Quantum Network ProtocolProgressive Swapping to the Middle (PSM) protocol methodology for efficient entanglement distribution in quantum networks with imperfect quantum memories. Combines nested entanglement swapping with memory-aware scheduling. Use when designing quantum network protocols, entanglement distribution strategies, quantum repeater architectures, or optimizing quantum communication under memory decoherence constraints. Activation: progressive swapping, PSM protocol, quantum memory entanglement distribu...Votes: 0GitHub stars: 3
- Project DealProject DealVotes: 0GitHub stars: 3
- Project Fetch AutonomousMethodology for evaluating autonomous AI capability progression across phases — measuring when models transition from human-assisted to autonomous execution using robotics tasks as a benchmark.Votes: 0GitHub stars: 3
- Project Fetch Phase TwoAnthropic research (Jun 18, 2026) — Project Fetch phase two showing Claude Opus 4.7 autonomously completing robotic quadruped tasks 20x faster than the fastest human team, revealing the progression pattern from "models helpful to humans" → "humans helpful to models" → "models autonomous."Votes: 0GitHub stars: 3
- Project Pilot Ai Drone ControlProject Pilot — methodology for testing AI control of physical systems like drones through constrained interfaces and safety protocols. Based on Anthropic's July 2026 frontier red teaming research.Votes: 0GitHub stars: 3
- Projector Variational AnsatzProjector Variational Ansatz (PVA) methodology for VQE that bridges NISQ variational and FTQC algorithm structures. Combines shallow ansatz depth with projector-based ground state identification.Votes: 0GitHub stars: 3
- Properties Of The After Kernel**arXiv ID:** 2105.10585 **Authors:** Philip M. Long **Published:** 2021-05-21T21:50:18Z **Abstract:** The Neural Tangent Kernel (NTK) is the wide-network limit of a kernel defined using neural networks at initialization, whose embedding is the gradient of the output of the network with respect to its parameters. We study the "after kernel", which is defined using the same embedding, except after training, for neural networks with standard architectures, on binary classification problems extr...Votes: 0GitHub stars: 3
- Proposal And Verification Of Novel Machine Learning On Classification Problems**arXiv ID:** 2207.04884 **Authors:** Chikako Dozono, Mina Aragaki, Hana Hebishima, Shin-ichi Inage **Published:** 2022-06-23T06:47:32Z **Abstract:** This paper aims at proposing a new machine learning for classification problems. The classification problem has a wide range of applications, and there are many approaches such as decision trees, neural networks, and Bayesian nets. In this paper, we focus on the action of neurons in the brain, especially the EPSP/IPSP cancellation between excita...Votes: 0GitHub stars: 3
- Prospective Coding Path Integration Self Organizing前瞻编码与路径整合的自组织神经网络框架。揭示连续吸引子网络(CANNs)如何通过赫布塑性、发放率适应和全局抑制自组织形成，实现前瞻性编码和路径整合功能。Votes: 0GitHub stars: 3
- Protecting Feedforward Networks From Adversarial Attacks Using Predictive Coding**arXiv ID:** 2411.00222 **Authors:** Ehsan Ganjidoost, Jeff Orchard **Published:** 2024-10-31T21:38:05Z **Abstract:** An adversarial example is a modified input image designed to cause a Machine Learning (ML) model to make a mistake; these perturbations are often invisible or subtle to human observers and highlight vulnerabilities in a model's ability to generalize from its training data. Several adversarial attacks can create such examples, each with a different perspective, effectiveness, ...Votes: 0GitHub stars: 3