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- Quantum Systems EngineeringSystems engineering patterns for quantum computing systems. Covers hybrid quantum-classical architecture design, distributed quantum computing, robust quantum control systems, and quantum system modeling. Use when designing quantum computing systems, analyzing distributed quantum architectures, or implementing robust control for quantum hardware. Also covers: dissipative thermal state preparation with error bounds, analytical two-pulse molecular qubit control, adaptive feedback for bistable q...Votes: 0GitHub stars: 3
- Quantum Systems Engineering ApproachSystems engineering methodology for quantum processor design. Integrates hardware architecture, control software, error correction, and resource management into a unified engineering framework. Covers scalability, reliability, and performance optimization for quantum systems.Votes: 0GitHub stars: 3
- Quantum Stochastic Walk PortfolioQuantum Stochastic Walk (QSW) optimizer for portfolio optimization — embeds assets in weighted covariance graph, derives weights from walk stationary distribution, achieving 15% Sharpe improvement and 90% turnover reduction vs classical mean-variance.Votes: 0GitHub stars: 3
- Quantum Software Engineering PracticeQuantum software engineering practical patterns for FPGA-based quantum control systems with AI integration. Covers hardware-aware compilation, real-time feedback control, and fault-tolerant quantum computing workflows. Activation: quantum software engineering, FPGA quantum control, quantum AI integration, quantum compilation, real-time quantum feedback.Votes: 0GitHub stars: 3
- Quantum Rl Scuc QsampleHybrid quantum-classical reinforcement learning for Security-Constrained Unit Commitment (SCUC). Uses Bernoulli hybrid soft actor-critic (HSAC) with quantum-sampled feature augmentation for economic dispatch in power systems. Activation: quantum RL SCUC, unit commitment optimization, quantum-sampled features, hybrid soft actor-critic, economic dispatch quantum, power grid optimization quantumVotes: 0GitHub stars: 3
- Quantum Portfolio OptimizerQuantum computing portfolio optimization skill. Uses QAOA, quantum annealing, and hybrid quantum-classical methods for financial portfolio optimization with higher-order moments (skewness, kurtosis) and real-world constraints (cardinality, turnover limits). Activation: quantum portfolio, quantum optimization, QAOA portfolio, 量子组合优化, quantum finance optimization, portfolio optimization quantum.Votes: 0GitHub stars: 3
- Quantum Pde Option PricingEnd-to-end quantum PDE framework for derivative pricing. Use when: designing quantum algorithms for option pricing, solving high-dimensional financial PDEs on quantum hardware, comparing quantum vs classical pricing complexity, implementing Black-Scholes or Heston models on quantum circuits, or evaluating quantum advantage for financial derivative pricing. Covers: quantum PDE solvers, finite-difference discretization, gate complexity analysis, Clifford+T resource estimation, implied volatilit...Votes: 0GitHub stars: 3
- Quantum Neuromorphic ComputingQuantum neuromorphic computing framework combining quantum gates, memristive synapses, and quantum cognition for decision making. Use when: (1) analyzing quantum brain models, (2) implementing quantum neural networks, (3) studying quantum cognition mechanisms, (4) exploring memristive quantum synapses, (5) simulating quantum neuromorphic systems.Votes: 0GitHub stars: 3
- Quantum Network Osi StackQuantum-Converged OSI stack architecture — extending classical OSI with Layer 0 (Quantum Substrate) and Layer 8 (Cognitive Intent) for 7G quantum networks. Covers entanglement, teleportation, QKD, QEC, PQC, RIS, and semantic orchestration via LLMs and QML.Votes: 0GitHub stars: 3
- Quantum Native DatabaseQuantum-native database design methodology (Qute framework) treating quantum computation as first-class execution. SQL-to-quantum-circuit compilation, hybrid quantum-classical query optimization, selective quantum indexing, and fidelity-preserving storage. Applicable to quantum data systems, hybrid query engines, and quantum information retrieval.Votes: 0GitHub stars: 3
- Quantum Lbm SurrogateHybrid quantum-classical surrogate model for Lattice Boltzmann Method (LBM) collision dynamics. Uses parameterized quantum circuits with data re-uploading to implement partial Fourier series, recovering complete BGK collision dynamics across full physically admissible range of relaxation without retraining. Use when building quantum surrogates for PDE solvers, fluid dynamics, or any physics simulation requiring non-unitary operation approximation.Votes: 0GitHub stars: 3
- Quantum Image Encoding CompressionGate-efficient quantum medical image encoding using Fourier-based methods. Reduces gates by 4x compared to pixel count. Covers two compression techniques for large-scale medical imaging with negligible quality loss.Votes: 0GitHub stars: 3
- Quantum Finance PipelineHardware-aware quantum portfolio optimization pipeline pattern. Combines correlation-guided decomposition, constraint-aware QAOA mixers, and non-variational quantum optimization for large-scale financial problems.Votes: 0GitHub stars: 3
- Quantum Finance Hybrid WorkflowDesign and evaluate hybrid quantum-classical financial workflows across portfolio optimisation, derivative pricing, risk estimation, and post-quantum security. Applies four-step evaluative logic: identify bottleneck → specify quantum primitive → compare classical benchmark → assess realistic constraints. Based on the financial-computation stack framework.Votes: 0GitHub stars: 3
- Quantum Error Correction MethodsReusable 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 error correction codes, or comparing QEC approaches.Votes: 0GitHub stars: 3
- Quantum Economic Advantage CalculatorFramework for calculating quantum economic advantage timing — determines when quantum systems outperform classical computers on cost-equivalent basis for specific algorithms, incorporating error correction overhead, gate speeds, connectivity, and hardware roadmaps.Votes: 0GitHub stars: 3
- Quantum Control EngineeringEngineering patterns for reliable, efficient quantum control systems. Covers pulse-level gate optimization, real-time closed-loop QEC, dynamic decoder scheduling, physics-informed LLM control, and thermodynamic control optimization. Use when designing quantum control architectures, optimizing gate implementations, building real-time error correction systems, or managing quantum resource allocation. Keywords: quantum control, pulse optimization, QEC scheduling, fault-tolerant control, FPGA qua...Votes: 0GitHub stars: 3
- Quantum Compiler RoutingQuantum compiler qubit mapping and routing methodology for scalable quantum circuit compilation. Covers position graph abstraction, heuristic mapper optimization (SABRE), memoized congestion resolution, and architecture-aware compilation for heterogeneous quantum hardware including TI-QCCD (Trapped-Ion Quantum Charge-Coupled Device) systems. Use when: implementing quantum compilers, designing qubit mapping and routing algorithms, optimizing quantum circuit compilation for specific hardware ar...Votes: 0GitHub stars: 3
- Quantum Bayesian Game EquilibriumParameterized quantum circuit methodology for computing correlated equilibrium in Bayesian games. Addresses exponential growth in joint type-action space with quantum advantage.Votes: 0GitHub stars: 3
- Quantum Ai PatternsReusable 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. Covers QNN design, distributed quantum computing, AI-assisted error correction, and continuous-time quantum models. Triggers: quantum machine learning, QNN, quantum neural network, hybrid quantum-classical, quantum AI patterns, distributed q...Votes: 0GitHub stars: 3
- Qnrl Quantum Native Reinforcement LearningQuantum-Native Reinforcement Learning (QnRL) methodology — distributional RL framework that learns conditional distributions in Hilbert space via superimposed and entangled quantum states using the Quantum Amplitude Kickback (QuAK) algorithm. Achieves up to 82.9% higher evaluation scores with 94.3% fewer parameters compared to classical RL baselines.Votes: 0GitHub stars: 3
- Qfi Entanglement RobustnessInformation-geometric framework for analyzing entanglement robustness using quantum Fisher information (QFI). Establishes bounds on concurrence reduction caused by parameter uncertainty in entanglement generation. Use when analyzing quantum entanglement stability, quantum network reliability, quantum sensing precision, or parameter-dependent quantum operations. Activation: quantum Fisher information, entanglement robustness, concurrence bounds, quantum network reliability, quantum sensing pre...Votes: 0GitHub stars: 3
- Qaoa Qrl Vehicle RoutingHybrid QAOA-QRL methodology for vehicle routing optimization — integrating QAOA mixing/cost Hamiltonian layers into reinforcement learning policy networks for combinatorial logistics optimization.Votes: 0GitHub stars: 3
- Qaoa Manifold OptimizationRiemannian 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 when optimizing QAOA parameters, dealing with barren plateaus, or improving quantum optimization convergence.Votes: 0GitHub stars: 3