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SkillsMP has collected 4,114 skills from hiyenwong/ai_collection. Open a skill to review its source and details.
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Quantum 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…
Quantum computing complexity theory + mathematical structure analysis. Analyzes quantum circuits, algorithms, and information theory through mathematical frameworks including Lie algebra, complexity classes, and geometric methods. Use when studying: quantum…
Quantum computational sensing (QCS) methodology for task-specific information extraction combining quantum sensing with quantum computing. Use for binary classification sensing tasks, quantum-enhanced signal processing, and quantum-classical hybrid sensing…
Reusable patterns from quantum computing and quantum machine learning research. Covers distributed quantum computing, variational quantum algorithms, QML architectures, and quantum advantage verification. Use when analyzing quantum computing papers, designing…
End-to-end learning of quantum control on latent dynamical manifold using LSTM. Joint learning of system dynamics and control strategies in low-dimensional latent space, replacing iterative simulate-then-optimize paradigm. Activation: end-to-end quantum…
Scaling laws for meta-learning in quantum control — determining when adaptation justifies its overhead, few-shot pre-adaptation budget estimation, and OOD robustness patterns. Covers device heterogeneity, environmental drift, per-device calibration reduction,…
Software framework methodology for pulse-level quantum computing that bridges gate-based abstractions with hardware-aware optimization. Integrates quantum optimal control within quantum machine learning (QML), enabling composable ansatz constructions,…
Ravine analysis framework for Quantum Cost Landscapes (QCLs) using Nudged Elastic Band (NEB) algorithm. Identifies low-cost paths connecting local minima in VQA optimization, constructs ensemble predictions from ravine-structured QNNs, and introduces a…
Quantum learning theory for continuous-variable (bosonic) systems. Covers sample complexity analysis for learning non-Gaussian and Gaussian states, trace distance bounds via covariance matrices, Gaussian state testing, and efficient Gaussian process learning…
Quantum data center network design and entanglement distribution optimization. Analyze resource requirements for entanglement purification in multi-hop quantum networks.
Toolbox methodology for understanding the physics of quantum data management. Connects quantum device physical behavior to database problem structure and difficulty, evaluates quantum annealing for combinatorial optimization in data management. Use when…
Quantum-based diagnostic architecture methodology for robust medical image analysis using compact quantum feature representations. Combines quantum-inspired architectures with classical deep learning for enhanced diagnostic accuracy with fewer parameters.…
Framework for analyzing how quantum entanglement reshapes the geometry of quantum differential privacy, characterizing privacy-utility tradeoffs in quantum information processing systems.
Low-depth distributed quantum search algorithms for unordered database lookup. Splits Grover search across distributed quantum nodes to reduce circuit depth and NISQ noise. Use when implementing distributed quantum computing for database search, optimizing…
Quantum distributed computing algorithms based on classical snapshot theory. Extends Chandy-Lamport snapshot to quantum systems for implementing decomposable global quantum operations. Use when designing quantum distributed algorithms, quantum causality…
Quantum divide and conquer methodology combining classical dynamic programming with quantum search to achieve improved exponential base for NP-hard combinatorial optimization. Parameterized hybrid algorithms with tunable quantum-classical balance. Use when…
Assess whether Quantum Deep Learning (QDL) approaches can practically deliver advantages given current and projected hardware constraints. Based on systematic survey of quantum algorithms mapped to deep learning applications. Use when: (1) Evaluating QDL…
Geometric approach to zero-memory quantum dot reservoir computing — leverages intrinsic nonlinear dynamics of quantum dot arrays for temporal information processing without internal memory states. Use when working with quantum dot systems for reservoir…
Quantum economics methodology using economic action constant (hbar_E) as structural analogue to Planck's constant for modeling macroeconomic regime transitions under radical uncertainty.
Quantum machine learning data encoding selection methodology based on arXiv:2606.05387. Provides a three-axis taxonomy (cost-expressivity-robustness), depth-fidelity bounds under NISQ decoherence, and a five-regime decision framework for choosing optimal…
Quantum End-to-End Learning (QEL) methodology for contextual combinatorial optimization. First quantum computing-based end-to-end learning framework leveraging QAOA with context re-uploading phase-separator. Enables joint end-to-end training with stationarity…
Lightweight quantum-enhanced ResNet for coronary angiography (CAG) classification. Combines classical CNN backbones with variational quantum circuits for medical image classification. Use when: coronary angiography analysis, cardiac image classification,…
Quantum-enhanced distributed network sensing (DQN) using multiple quantum resources: catalysis, entanglement, and squeezing for multiphase estimation approaching Heisenberg limit. arXiv: 2605.19545.
Hybrid quantum-classical SVM methodology using quantum kernel methods for financial market prediction and pattern recognition in high-dimensional data. Use when building quantum ML models for financial forecasting, market prediction, or trading strategy…
Quantum entanglement-assisted distributed storage methodology — achieving 2x bandwidth reduction for oblivious updates using shared entanglement and CSS codes.
Methodology for quantum entanglement degree imaging using PET scanners — extracting C_QE biomarkers from annihilation photon polarization correlations via Compton scattering. Use when researching quantum entanglement medical imaging, PET biomarker…
Quantum entanglement verification methodology — detecting fake entanglement from imperceptible measurement deviations, with implications for quantum information security, quantum key distribution, and entanglement-based protocols.
Quantum error correction using gauge theories and quantum reference frames. Building QECC from lattice gauge theories (QED, QCD). Use when researching quantum error correction, gauge theory applications, or quantum computing reliability.
Reusable patterns from quantum error correction research. Covers RL-controlled QEC, fault-tolerant architectures, neutral-atom systems, Bacon-Shor codes, and loss-biased codes. Use when analyzing QEC papers, designing fault-tolerant quantum systems, selecting…
Quantum f-divergence contraction rate analysis methodology. Use when analyzing quantum channel convergence, strong data processing inequalities (SDPI), quantum information contraction bounds, or studying how quantum states approach equilibrium under noisy…
Quantum fault-tolerance verification methodology using symbolic execution for quantum error correction codes. Formal verification framework for proving fault-tolerance properties of QECC implementations. Use when analyzing quantum error correction, verifying…
Quantum Feature Amplification Network (QFAN) methodology for autoregressive quantum generative modeling with fixed qubit budget. Use when generating quantum images, designing quantum generative models, or reducing qubit requirements for quantum demonstrations.
Quantum Feature Amplification Network (QFAN) methodology for autoregressive quantum generative modeling. Decouples quantum register size from output dimension using fixed-size quantum circuits combined with classical autoregressive decoding. Use when…
Communication-efficient Quantum Federated Learning (QFL) methodology for privacy-sensitive healthcare. Introduces Hybrid QFL architecture with light-cone feature selection and dynamic centralized/decentralized aggregation switching. Use when designing…
Mode-independent sample complexity for fermionic classical shadows. Improves worst-case bound from O(√n log n) to O(η log η) using harmonic analysis on AIII symmetric space.
Quantum Feshbach engine methodology — optimization framework for high-efficiency quantum thermodynamic cycles using trapped Bose-Einstein condensates with Feshbach resonance tuning. Use when designing quantum heat engines, optimal control of quantum many-body…
Quantum state fidelity estimation methodology with optimal sample complexity bounds. Covers O(r²/ε²) upper and Ω(r/ε²) lower bounds for rank-r reference states, tolerant certification, and quantum query complexity implications. Use when estimating quantum…
Quantum computing applications in finance and economics. Use when analyzing quantum portfolio optimization, quantum Monte Carlo for risk, quantum game theory, option pricing with quantum algorithms. Keywords: quantum finance, quantum portfolio, quantum Monte…
Financial computation stack framework for evaluating quantum computing applications across five connected domains. Based on arXiv:2604.08180 (134-page review) plus 2026 hot-starting and benchmark papers.
Quantum Fisher Information (QFI) duality methodology for distributed quantum sensing — establishing fundamental trade-offs between sensing precision and parameter privacy.