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Showing 10,273–10,296 of 29,835 skills
- Rl Qec ControlReinforcement Learning for Quantum Error Correction control methodology. Based on Google Quantum AI's Willow processor framework (arXiv:2511.08493). Use when: (1) designing RL-based calibration for quantum systems, (2) implementing continuous error correction without halting computation, (3) repurposing QEC syndrome measurements as RL learning signals, (4) stabilizing quantum operations against environmental drift, (5) optimizing surface code or color code performance. Keywords: quantum error...Votes: 0GitHub stars: 3
- Ribbon Zx Calculus Gauge TheoryRibbon ZX calculus framework for gauge theory — extends ZX diagrammatic calculus to 2D Yang-Mills theory with compact gauge groups via Hopf Frobenius algebraic structure.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 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 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 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 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 Renormalization GoursatQuantum renormalization group flow methodology for 1D mixed states using C*-Hopf algebra representations. Perturbs renormalization fixed points with on-site noise quantum channels, coarse-grains iteratively, and describes effective flows via quantum Goursat lemma. Connects renormalization, topological order, and quantum information theory. Activation: quantum renormalization flow, Goursat lemma quantum, topological boundary states, C* Hopf algebra, noise channel coarse graining, mixed state RGVotes: 0GitHub stars: 3
- Quantum Reliability PathwaysElectromagnetic-to-architecture reliability prediction methodology for superconducting quantum processors. Use when: (1) predicting quantum processor reliability from physical layout and electromagnetic design, (2) analyzing how design distortion modifies effective Hamiltonian and mediated connectivity, (3) estimating architectural reliability early in quantum chip design, (4) connecting electromagnetic design choices to execution-level behavior, (5) EPAR framework for quantum architectural r...Votes: 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 Os Resource ManagementQuantum operating systems and resource management patterns for hybrid quantum-classical computing. Covers QOS architecture, Slurm-based quantum resource scheduling, job multiprogramming, hardware-agnostic APIs, error mitigation at the OS level, and container management for quantum workloads. Use when: (1) Designing quantum operating systems or resource managers, (2) Integrating quantum backends with HPC schedulers (Slurm, Kubernetes), (3) Building hardware-agnostic quantum job execution APIs,...Votes: 0GitHub stars: 3
- Quantum Off Policy Evaluation PricingQuantum off-policy evaluation (OPE) methodology for insurance pricing and financial decision optimization. Applies quantum reinforcement learning, quantum IPS estimators, and variational quantum circuits to pricing problems. Based on arXiv:2605.28327 (Insurance Pricing Optimization via Off-Policy Evaluation). Activation: quantum pricing, off-policy evaluation, quantum OPE, insurance pricing optimization, quantum reinforcement learning pricing, quantum IPS.Votes: 0GitHub stars: 3
- Quantum Neuromorphic ComputingQuantum neuromorphic computing framework combining quantum gates, memristive synapses, quantum cognition, optical quantum neurons, and neuromorphic quantum kernels. 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, (6) building HOM interference-based optical quantum neurons, (7) comparing neuromorphic vs parameterized quantum ...Votes: 0GitHub stars: 3
- Quantum Neural ArchitectureQuantum Neural Network (QNN) architecture design and optimization patterns. Covers quantum-classical hybrid learning, Lie algebra truncation, barren plateau mitigation, quantum expressivity, and tensor network approaches. Activates for: QNN design, quantum neural network, quantum machine learning, quantum-classical hybrid, quantum expressivity phase transition, LieTrunc, quantum gradient descent.Votes: 0GitHub stars: 3
- Quantum Network Task ControlCentralized task-based quantum network control framework. Resource-centric approach replacing layered protocol stacks. Centralized controller tracks quantum memory availability across nodes and schedules objectives via priority-based scheduler. Use when: quantum network architecture, centralized quantum control, task-based quantum networking, quantum memory scheduling, SeQUeNCe simulator, quantum network scaling, arXiv:2605.03336.Votes: 0GitHub stars: 3
- Quantum Network SchedulingQuantum network resource allocation and entanglement flow scheduling. Use when designing, optimizing, or analyzing quantum network architectures involving entanglement distribution, multi-channel resource allocation, queuing mechanisms for quantum requests, or classical allocation algorithms (Dynamic Efficient, Longest Queue First, Weighted LQF) applied to quantum networks. Also relevant for quantum-classical hybrid system scheduling and entanglement routing in distributed quantum computing.Votes: 0GitHub stars: 3
- Quantum Network ControlOptimize entanglement distribution in quantum networks via link-layer control strategies. Compare sequential vs simultaneous entanglement swapping for multi-hop quantum communication. Use when: (1) designing quantum network architectures, (2) optimizing entanglement distribution, (3) comparing quantum repeater strategies, (4) quantum internet protocol design, (5) entanglement swapping optimization, (6) quantum network link-layer control.Votes: 0GitHub stars: 3
- Quantum Logic Ai FinanceQuantum logic framework for human-centric AI in finance, extending classical rationality to contextual reasoning using quantum-inspired neural networks for algorithmic trading, portfolio management, and robo-advisory. Based on arXiv:2510.05475v1.Votes: 0GitHub stars: 3
- Quantum Linear Matrix DifferentialEfficient quantum algorithm for solving linear matrix differential equations with applications to open quantum system simulation. Computes solution matrix entries with query complexity O~(νLt/ε), achieving nearly optimal scaling. Use when: quantum simulation of open systems, linear differential equation solvers, quantum dynamics simulation, dissipative quantum systems, quantum Carleman linearization.Votes: 0GitHub stars: 3
- Quantum Inspired OptimizationQuantum-Inspired Evolutionary Optimization (QIEO) for non-convex ML optimization. Uses quantum superposition-inspired probability amplitudes, quantum rotation gates for distribution updates, and quantum interference for exploration/exploitation balance. Use when: quantum-inspired optimization, QIEO, non-convex optimization, global search evolutionary, escaping local minima, 量子启发优化, 量子进化优化.Votes: 0GitHub stars: 3
- Quantum Hyperdimensional Computing QhdcQuantum Hyperdimensional Computing (QHDC) is a foundational paradigm for quantum neuromorphic architectures introduced in arXiv:2511.12664. It demonstrates that the core operations of classical Hyperdimensional Computing (HDC) — a brain-inspired model — map with remarkable elegance to the native operations of a quantum computer.Votes: 0GitHub stars: 3
- Quantum Graph Machine Learning ModelsGeometric Quantum Machine Learning (GQML) design toolbox for graph problems — comprehensive characterization of constituents for n-node-graph → n-qubit-state encoding; enables hybrid classical-quantum integration, generalizes known GQML models (extending expressivity at near-zero cost), and supports straightforward classical pre-training; validated numerically.Votes: 0GitHub stars: 3
- Quantum Genetic Negative SelectionQuantum Genetic Negative Selection Algorithm (QGNSA) methodology for anomaly detection using quantum-enhanced evolutionary optimizationVotes: 0GitHub stars: 3
- Quantum FinanceQuantum computing applications in finance: portfolio optimization, option pricing, risk management, financial simulations, and quantum economics using quantum algorithms (QAOA, quantum annealing, quantum Monte Carlo, amplitude estimation, entangled neural traders). Use for quantum finance research, NISQ-era financial applications, quantum advantage analysis in derivatives/derivatives pricing, and economic action constants.Votes: 0GitHub stars: 3