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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 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…
Riemannian 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…
Constraint-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;…
QAOA + 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,…
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.
Source text: Chinese
QDiffusion-TS - First quantum generative diffusion model for time series synthesis with real quantum hardware validation on IQM processor
QKAN (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…
Framework 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.
SoK: 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…
Quantum 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…
Dynamical 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…
Multidimensional 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…
Systematic mutation testing methodology for quantum machine learning models. Use when testing, validating, or assessing robustness of quantum ML models, variational quantum circuits, and QNNs.
Quantum Measurement Temperature (QMT) methodology for stabilizing hybrid QNN training. Addresses measurement-induced logit contraction in variational quantum classifiers for protein and medical image classification.
Quantum 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…
Quantum 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…
qReduMIS 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.
Quantum 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…
Quantum 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.…
Mutation 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,…
Quantized time statistics methodology for quantum walks under weak rank-K measurements using topological winding numbers
Rigorous 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…
Quantum 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)…
Passive 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…
Quantum 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…
Reusable 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.…
Quantum 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,…
[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.]
Quantum analog-encoding methodology for correlated Gaussian vectors and rough volatility simulation. Uses QSVT framework to prepare quantum states representing correlated Gaussian distributions with O(log N) gate complexity. Applicable to financial modeling…
Critical audit methodology for quantum annealer portfolio optimization pipelines. Reveals structural failures in standard penalty-encoded QUBO formulations (chain-break fractions 83-92% on D-Wave Pegasus/Zephyr) and quantifies actual QPU usage in hybrid…
Quantum annealing-based feature selection for interpretable AI in Convolutional Neural Networks. Uses constrained optimization to select most important feature maps contributing to predictions, providing explainable AI with improved class disentanglement. Use…
Quantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI. Uses angle encoding, variational encoder-decoder with trash qubits, and incompressibility-based anomaly scoring. Achieves ROC-AUC ~0.95 slice-level and ~0.813 patch-level with…
Quantum algorithms for Bayesian state estimation and transport dynamics prediction. Use when: (1) implementing Bayesian filtering/prediction on quantum computers, (2) solving Fokker-Planck equations via quantum algorithms, (3) encoding probability…
Quantum block encoding methodology for Difference-of-Gaussian (DoG) operators on periodic grids. Implements Linear Combination of Unitaries (LCU) framework without black-box oracles. Activation: quantum block encoding, DoG operator, quantum machine learning,…
Quantum Boltzmann Machine via Bilevel Optimization methodology. Extends QAOA circuit to bilevel optimization for fully connected QBMs, overcoming the fixed target Hamiltonian barrier. Use when building quantum generative models, training quantum Boltzmann…
Quantum-enhanced LLM methodology using Cayley-parameterized unitary adapters to overcome classical memory scaling limits. Enables quantum circuit blocks in frozen transformer architectures for LLM fine-tuning on real quantum hardware.
[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.]
Design and implement quantum-classical hybrid neural networks that combine quantum computing with classical deep learning. Use when working with quantum machine learning (QML), quantum neural networks (QNN), hybrid quantum-classical architectures, or when…
经典与量子电磁理论中的不确定性关系。推导位置和动量（波矢）空间中方差尖锐不确定关系，统一形式ΔrΔk≥5/2适用于经典光束、量子相干光束和单光子理论。揭示经典-量子对应中的不确定性结构。
Source text: Chinese
Quantum cloning-learning equivalence methodology — proves that for structured quantum state classes (n-qubit stabilizer states), the optimal sample complexity of cloning equals that of learning: Theta(n). Uses Abelian State Hidden Subgroup framework and…