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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Quantum Sequence SamplersQuantum sequence models for learning and generating stochastic processes. Quantum circuits that generate coherent superpositions of stochastic processes enable quantum-accelerated risk analysis, importance sampling, and DNA sequencing. Addresses the challenge of encoding classical stochastic processes into quantum states efficiently.Votes: 0GitHub stars: 3
- Quantum Side Channel LeakageQuantum-based side-channel leakage verification methodology. Uses quantum algorithms for verifying side-channel countermeasures against leakage attacks. Applicable to cryptographic hardware security, side-channel analysis, and countermeasure validation.Votes: 0GitHub stars: 3
- Quantum Signal Processing Orthogonal PolyQuantum Signal Processing (QSP) via orthogonal polynomial theory methodology. Provides analytical angle-finding for QSP protocols using Hermite, Jacobi, and Rogers-Szego polynomial expansions. Enables block-encoding of smooth functions with O(log(1/epsilon)) gate complexity. Use when: implementing quantum signal processing, quantum algorithm design with orthogonal polynomials, quantum function approximation, Hamiltonian simulation, quantum eigenvalue transformation, or designing efficient qua...Votes: 0GitHub stars: 3
- Quantum Signal Processing Orthogonal PolynomialsImplement Quantum Signal Processing (QSP) using orthogonal polynomial theory. Derive QSP angles analytically for Hermite, Jacobi, and Rogers-Szego polynomial families. Achieve O(log(1/ε)) gate complexity for ε-approximation of smooth functions via Hermite series expansion. Use when implementing QSP circuits, finding QSP angles, approximating functions via quantum signal processing, or connecting orthogonal polynomials to quantum algorithms. arXiv: 2605.05321Votes: 0GitHub stars: 3
- Quantum Software ArchitectureComponent-based Quantum Software Architecture Framework (QSAF) for designing hybrid quantum-classical systems. Provides 34 reusable quantum circuit patterns, architectural guidelines, and systematic transition from circuit-level to system-level design.Votes: 0GitHub stars: 3
- Quantum Software EngineeringResearch software engineering (RSE) methodology for quantum and scientific computing codes. Covers continuous integration, automated testing, compiler warning correction, continuous benchmarking, and detection of critical defects in scientific software. Applicable to Fortran, C/C++, and any high-performance scientific computing project.Votes: 0GitHub stars: 3
- Quantum Solver EvaluationQuantum solver evaluation and quantum neural network assessment methodology. Covers Q-SAGE iterative quantum solver generation evaluation, equivariant RL for Clifford circuit synthesis, photonic QNN algorithmic advantage assessment, LUNA LUT-based qubit readout, and CliNR mid-circuit noise reduction. Use when: evaluating LLM-generated quantum code, synthesizing quantum circuits with RL, benchmarking QNNs vs ANNs, designing low-latency qubit readout, or reducing noise in Hamiltonian simulation...Votes: 0GitHub stars: 3
- Quantum Sparsity Edge ChaosQuantum sparsity design principle for robust VQAs using topological Entanglement Entropy (TEE) as cost function regularizer. Guides optimization along the critical edge of chaos, mitigating barren plateaus in Variational Quantum Algorithms. Activation: quantum sparsity, edge of chaos, TEE regularizer, entanglement entropy, VQA convergence, barren plateau mitigation, quantum Nyquist-Shannon.Votes: 0GitHub stars: 3
- Quantum Spectral MlQuantum spectral methods for machine learning - leveraging quantum computing's natural ability to manipulate Fourier spectrum for ML tasks. Use when exploring quantum ML algorithms, spectral methods in quantum context, or Fourier-based quantum ML approaches. Activation: quantum spectral, quantum ML, 量子谱方法, quantum Fourier, spectral ML.Votes: 0GitHub stars: 3
- Quantum Spectral PdeQuantum Spectral Framework for solving PDEs using Quantum Block Encoding (QBE) with quantum reversible arithmetic. Exploits filter structure in Fourier space to solve second-order linear PDEs. Extends to quantum group Fourier transforms, wavelet-based analysis, and equivariant quantum neural networks (EQNNs). Use when: quantum PDE solvers, quantum spectral methods, QBE for differential equations, quantum Fourier analysis for PDEs, EQNN applications, arXiv:2604.25825.Votes: 0GitHub stars: 3
- Quantum Stabilizer Code SurgeryCompiler techniques for synthesizing resource-efficient code surgery protocols on arbitrary quantum stabilizer codes. Covers structure-aware graph optimization, ancilla qubit reduction, dynamic expansion-congestion balancing, and code degree constraints for fault-tolerant logical operations. Use when: designing fault-tolerant quantum operations, synthesizing code surgery for stabilizer codes, optimizing ancilla overhead in QLDPC codes, implementing cross-code logical communication, or reducin...Votes: 0GitHub stars: 3
- Quantum State Preparation NnNeural network-based quantum state preparation methodology from arXiv:2605.31006. Trains classical neural networks to map input data directly to quantum circuit parameters, avoiding per-instance variational optimization. Achieves 0.992 fidelity on unseen images with 5000x runtime reduction.Votes: 0GitHub stars: 3
- Quantum Statistical EstimationQuantum statistical estimation theory and applications - combines Bayesian methods, quantum Cramér-Rao bounds, and quantum parameter estimation for optimal quantum system state estimation. Use when analyzing quantum metrology, quantum parameter estimation, quantum statistics theory, or implementing optimal measurement strategies for quantum systems.Votes: 0GitHub stars: 3
- Quantum Statistical MetrologyQuantum statistical metrology methodology for multi-parameter quantum estimation using purification-based strategies. Covers quantum Cramér-Rao bounds, Holevo bounds, and sequential hypothesis testing for quantum state discrimination.Votes: 0GitHub stars: 3
- Quantum Statistics Collider QubitQuantum statistics measurement in extended collider systems coupled to qubits. Methodology for probing mutual statistics of quantum particles using mesoscopic colliders with qubit coupling. Use when analyzing quantum statistics, anyon detection, quantum point contacts, or mesoscopic collider experiments. Activation: quantum statistics, collider, qubit coupling, mutual statistics, anyon detection, quantum point contact, mesoscopic colliderVotes: 0GitHub stars: 3
- Quantum Structure In Ai LanguageQuantum-like structure in AI language: Bell inequality violation and Bose-Einstein statistics in LLMs. Use when analyzing non-classical probability in language models, quantum cognition applied to AI, conceptual combinations in LLMs, evolutionary convergence of human and artificial cognition, or quantum statistical patterns in text generation.Votes: 0GitHub stars: 3
- Quantum Subgradient CvarQuantum subgradient estimation methodology for Conditional Value-at-Risk (CVaR) minimization using amplitude estimation. Provides near-quadratic query complexity improvement O(1/eps) vs O(1/eps^2) classical Monte Carlo for tail-risk optimization. Use when implementing quantum risk management, portfolio CVaR optimization, quantum amplitude estimation for financial risk, or quantum stochastic optimization.Votes: 0GitHub stars: 3
- Quantum Symmetry Reservoir ComputingSymmetry exploitation methodology for Quantum Reservoir Computing. Shows that symmetric Hamiltonian alone is insufficient for symmetry matching; introduces observable-orbit completion that aligns encoding, dynamics, measurement, and readout interfaces. Use when building QRC systems for cyclic/symmetric time series data or sensor networks.Votes: 0GitHub stars: 3
- Quantum Symmetry StructuresCross-disciplinary analysis of mathematical symmetry principles in quantum physics. Combines number theory, statistics, algebraic geometry with quantum mechanics. Covers: mirror dual symmetry (Rabi-Dirac), symplectic forms (L∞-Lagrangian), quantum memory control synthesis (Pauli structure), and quantum noise optimization. Activation: quantum symmetry, quantum structure, quantum数学, symmetry analysis, quantum algebra, quantum geometry.Votes: 0GitHub stars: 3
- Quantum Syndrome Adaptive DecodingAdaptive syndrome processing for quantum error correction decoding. Dynamically adjusts decoder parameters based on syndrome patterns and noise characteristics to improve logical qubit fidelity.Votes: 0GitHub stars: 3
- Quantum System ArchitectureQuantum system architecture design skill - systems engineering approach to quantum computing. Covers hybrid quantum-classical systems, dataflow frameworks, distributed quantum computing models, fault-tolerant architecture (FTQC), resource optimization strategies, and quantum error correction with gauge theory. Use for: (1) Designing hybrid quantum-classical computing systems, (2) Dataflow graph program representation, (3) Distributed quantum network architecture, (4) FTQC floorplan optimizati...Votes: 0GitHub stars: 3
- Quantum System Engineering量子系统工程方法论 - 涵盖分布式量子计算、混合量子-经典系统架构、量子错误纠正、量子系统优化。适用于量子计算系统设计、量子网络架构、量子-经典混合工作流等任务。关键词：quantum systems, distributed quantum computing, quantum architecture, hybrid quantum-classical, quantum error correction, qubit design, quantum network, 量子系统工程, 分布式量子计算, 量子架构设计。Votes: 0GitHub stars: 3
- Quantum Systems Control SimulationQuantum systems control theory and simulation framework. Covers coherent feedback control (H∞), physics-informed discrete-event simulation for quantum networks, and high-dimensional quantum photonics encoding. Use when: (1) designing control systems for quantum linear systems, (2) simulating polarization-encoded quantum networks, (3) implementing H∞ disturbance attenuation, (4) encoding quantum states in high-dimensional photonic modes, (5) analyzing quantum network stability and performance.Votes: 0GitHub stars: 3
- Quantum Systems EngineeringSystems engineering patterns for quantum computing systems. Covers hybrid quantum-classical architecture, distributed quantum computing, robust quantum control, QEC network architectures (SCOPE Pattern 20), network constraint modeling (Pattern 21), QLDPC logical processing (Pattern 14), ML-based QEM (Pattern 15), deterministic cat state generation (Pattern 16), software-based coherent error compensation (Pattern 17), hardware-tailored QEC resource estimation (Pattern 18), QND measurement prim...Votes: 0GitHub stars: 3
- Quantum Tensor Network MlQuantum tensor network methods for many-body quantum systems analysis. Combines belief propagation algorithms, tensor network expansions, and machine learning for efficient quantum state representation and computation. Use when: (1) analyzing many-body quantum systems, (2) designing tensor network architectures, (3) implementing belief propagation for quantum states, (4) compressing quantum state representations, (5) studying quantum entanglement patterns.Votes: 0GitHub stars: 3
- Quantum Tensor Network SimulationQuantum tensor network simulation optimization with PTSBE (Pre-Trajectory Sampling with Batched Execution). Accelerates quantum trajectory methods for noisy quantum systems. Use when: (1) Simulating noisy quantum circuits, (2) Optimizing tensor network quantum simulations, (3) Implementing batched sampling for quantum statevectors, (4) Reducing computational overhead in quantum density matrix simulations.Votes: 0GitHub stars: 3
- Quantum Tensor Train SurrogatesLocal tensor-train surrogates methodology for quantum machine learning models. Constructs fast, cheap, provably accurate classical surrogates of fully trained QML models within local patches of input data space. Combines Taylor polynomial approximation with tensor-train representation via empirical risk minimization. Use when implementing efficient quantum ML inference acceleration, tensor-train approximation of quantum circuits, or local surrogate modeling for QML.Votes: 0GitHub stars: 3
- Quantum Time Lower BoundsQuantum Time Lower Bounds by Permutation Invariance. Use when analyzing quantum algorithms, complexity bounds, quantum ML architectures, or quantum error correction involving mathematical analysis and statistical methods.Votes: 0GitHub stars: 3
- Quantum Tomography Retrodiction UnifiedUnified framework for quantum tomography and quantum retrodiction — proves Petz recovery map equals gradient update of log-likelihood in maximum-likelihood tomography, with noncommutative generalization for arbitrary quantum channels. Use when working with quantum tomography, quantum retrodiction, Petz recovery map, maximum-likelihood estimation, quantum channel inference, statistical inference in quantum systems, or gradient-based quantum state reconstruction.Votes: 0GitHub stars: 3
- Quantum Topological AnalysisQuantum and classical algorithms for topological data analysis (TDA) including persistent Betti numbers computation, simplicial complex construction, and persistence diagram interpretation. Use when analyzing topological features of data, persistent homology, Betti numbers, simplicial complexes, or topological data analysis. Triggers: TDA, 拓扑数据分析, Betti numbers, Betti数, persistent homology, 持久同调, simplicial complex, 单纯复形, quantum TDA, quantum topology.Votes: 0GitHub stars: 3
- Quantum Topological Data AnalysisQuantum algorithms for topological data analysis (TDA) - persistent Betti numbers, simplicial complexes, Vietoris-Rips topology, high-dimensional feature extraction. Use when analyzing quantum approaches to TDA, persistent homology, Betti number estimation, topological quantum computing, or geometry-informed quantum algorithms.Votes: 0GitHub stars: 3
- Quantum Topology SpectroscopyQuantum topology spectroscopy methodology for detecting band topology via quantum optical signatures in high-harmonic generation (HHG). Use when: (1) analyzing topological phases via optical spectroscopy, (2) computing high-harmonic generation in solid-state systems, (3) studying quantum light signatures of band topology, (4) implementing density-matrix evolution for light-matter dynamics, (5) designing topology-sensitive quantum light sources. Based on arXiv:2604.20388 (Ilin, Solntsev, Iorsh...Votes: 0GitHub stars: 3
- Quantum Transition State MethodologyQuantum transition state methodology — finding exact quantum counterparts to classical transition states using quantum flow geometry. Use when analyzing quantum reaction dynamics, tunneling rates, or quantum-classical correspondence in chemical physics. Activation: quantum transition state, quantum flow, recrossing-free flux, transition-state geometry, quantum reaction dynamics, 量子过渡态Votes: 0GitHub stars: 3
- Quantum Transport ClusteringQlustering: Unsupervised clustering via steady-state quantum transport in GKSL-governed quantum networks. Data encoded as input states, cluster assignments inferred from terminal output currents. Use when: quantum machine learning, unsupervised quantum clustering, GKSL master equation applications, open quantum network learning, quantum data clustering, or algorithm-hardware co-design for quantum ML.Votes: 0GitHub stars: 3
- Quantum Transport Statistics FrameworkExact framework for computing heat, energy, and particle transport statistics in quadratic quantum systems coupled to Gaussian reservoirs — combines full counting statistics with non-Markovian master equations. Use when: analyzing quantum transport in mesoscopic systems, computing full counting statistics for particle/heat currents, studying non-Markovian open quantum systems, evaluating transport between quantum reservoirs, or modeling quantum thermodynamic engines.Votes: 0GitHub stars: 3
- Quantum Triangle SparsificationQuantum algorithms for graph triangle cut sparsification methodology. Uses quantum walks and Grover search to list triangles faster than classical bounds, enabling efficient construction of ε-sparsifiers for large-scale network analysis.Votes: 0GitHub stars: 3
- Quantum Tug Of War DecisionQuantum Tug-of-War (QTOW) decision making model — contextuality arises generatively from physically grounded constraints on decision dynamics. Conservation-based internal state updates and measurement-induced disturbance produce KCBS-type contextuality witnesses. Proves quantum probability is structurally necessary for adaptive decision dynamics, not merely descriptive. Use when: quantum decision making, contextuality in choices, TOW model, non-Kolmogorovian probability, adaptive learning dyn...Votes: 0GitHub stars: 3
- Quantum Tunneling OptimizationQuantum-inspired evolutionary optimization for non-convex ML landscapes using superposition-inspired probabilistic encoding and simulated tunneling to escape local optima. Use when classical optimizers (ADAM, GA, DE) get stuck in local minima on sparse signal recovery, robust regression, or any non-convex objective. Triggers: non-convex optimization, local optima escape, quantum tunneling optimizer, sparse signal recovery, robust regression, quantum evolutionary algorithm, superposition-inspi...Votes: 0GitHub stars: 3
- Quantum Viterbi DecodingQuantum Viterbi decoding methodology for hidden quantum Markov models (HQMMs). Extends classical Viterbi algorithm to quantum sequential decision-making with proven advantage over classical diagonal strategies.Votes: 0GitHub stars: 3
- Quantum Young Measure HomogenizationQuantum algorithm methodology for nonlinear and stochastic homogenization using Young-measure based linear programming formulation with provable quantum speedup.Votes: 0GitHub stars: 3
- Qubo Hybrid Optimization SchedulingQUBO-based hybrid quantum-classical optimization methodology for coordinated scheduling problems. Formulates time-dependent operational constraints as quadratic unconstrained binary optimization, then solves with quantum annealing validated by classical simulation. Use when formulating scheduling/assignment problems for quantum optimization, building hybrid quantum-classical solvers, or modeling time-dependent constraints in QUBO form.Votes: 0GitHub stars: 3
- Qudit Encoding Quantum OptimizationQudit encoding methodology for variational quantum optimization of integer and multi-valued problems. Demonstrates exponential Hilbert space reduction vs binary qubit encoding while maintaining or improving optimization performance. Covers qudit-native QAOA, integer optimization, scheduling problems, and resource-efficient quantum encodings. Activation: qudit encoding, quantum integer optimization, qudit QAOA, multi-valued quantum, Hilbert space reduction, fleet management optimization, vehic...Votes: 0GitHub stars: 3
- Quiet Edge Centric Brain SynchronizationQUIET (Quantifying Underutilized Influential Edges for Targeted Synchronization) is an edge-centric framework that integrates structural controllability and functional connectivity to identify energy-efficient synchronization pathways in brain networks.Votes: 0GitHub stars: 3
- Qumvqd Quantum ChemistryQumode-based Variational Quantum Deflation (QumVQD) framework for excited-state quantum chemistry on bosonic quantum processors. Computes electronic and vibrational excited state energies with 1-2 orders lower gate counts than qubit-based methods. Keywords: quantum chemistry, excited states, qumode, bosonic quantum processor, variational quantum deflation, VQD, vibrational structure, electronic structure.Votes: 0GitHub stars: 3
- Qutrit Entropy EstimationVon Neumann entropy estimation in multi-qutrit quantum systems via variational quantum algorithms and classical neural networks. Use when estimating quantum entropy for d-level systems (qudits), selecting VQA ansatze for entropy estimation, or benchmarking quantum vs classical approaches for quantum information metrics. Covers SU(3)-inspired hardware-efficient ansatze, parameter sweep methodology, and CNN-based density matrix entropy estimation.Votes: 0GitHub stars: 3
- Radon Sobolev Variational Autoencoders**arXiv ID:** 1911.13135 **Authors:** Gabriel Turinici **Published:** 2019-11-29T15:02:28Z **Abstract:** The quality of generative models (such as Generative adversarial networks and Variational Auto-Encoders) depends heavily on the choice of a good probability distance. However some popular metrics like the Wasserstein or the Sliced Wasserstein distances, the Jensen-Shannon divergence, the Kullback-Leibler divergence, lack convenient properties such as (geodesic) convexity, fast evaluation a...Votes: 0GitHub stars: 3
- Ramanujan Hypergraph Quantum RoutingBlock permutation routing on Ramanujan hypergraphs for fault-tolerant quantum computing. Use when: routing surface code patches on reconfigurable lattices, analyzing quantum circuit compilation overhead, designing fault-tolerant qubit movement protocols, spectral analysis of quantum connectivity graphs. Keywords: quantum routing, Ramanujan hypergraph, surface code, fault-tolerant quantum computing, block permutation, lattice surgery, spectral graph theory.Votes: 0GitHub stars: 3
- Random Dimension Reduction Quantum LearningRandom dimension reduction methodology for learning symmetric properties of quantum states. Black-box procedure that replaces dimension with maximum rank in sample complexity. Use when learning symmetric quantum properties, estimating state distances/fidelities, or reducing quantum tomography overhead.Votes: 0GitHub stars: 3
- Random Projection Tree Similarity Metric For Spectralnet**arXiv ID:** 2302.13168 **Authors:** Mashaan Alshammari, John Stavrakakis, Adel F. Ahmed, Masahiro Takatsuka **Published:** 2023-02-25T21:32:16Z **Abstract:** SpectralNet is a graph clustering method that uses neural network to find an embedding that separates the data. So far it was only used with $k$-nn graphs, which are usually constructed using a distance metric (e.g., Euclidean distance). $k$-nn graphs restrict the points to have a fixed number of neighbors regardless of the local stati...Votes: 0GitHub stars: 3
- Rank Order N Of M Codes For Sparse Distributed MemDerived from arXiv:2607.02967 - Rank-Order N-of-M Codes for Sparse Distributed Memory: Disentangling Representation and Learning Effects in Noise Robustness Against Contemporary Neuromorphic ArchitecturesVotes: 0GitHub stars: 3