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Dave AI
Quantum Kernel Estimator
Quantum kernel computation skill for quantum machine learning
instructions.md
--- name: quantum-kernel-estimator-777abf1104b2 description: Quantum kernel computation skill for quantum machine learning allowed-tools: - Bash - Read - Write - Edit - Glob - Grep metadata: specialization: quantum-computing domain: science category: quantum-ml phase: 6 graph: domains: [domain:quantum-computing] specializations: [specialization:quantum-computing] skillAreas: [skill-area:mathematical-reasoning, skill-area:machine-learning-frameworks, skill-area:statistical-analysis] workflows: [workflow:experiment-design] roles: [role:research-scientist, role:ml-engineer] --- # Quantum Kernel Estimator ## Purpose Provides expert guidance on quantum kernel methods for machine learning, enabling kernel-based classifiers and regressors with quantum feature maps. ## Capabilities - Fidelity quantum kernel - Projected quantum kernel - Kernel alignment optimization - Feature map design - SVM integration with quantum kernels - Kernel matrix visualization - Bandwidth tuning - Trainable kernel circuits ## Usage Guidelines 1. **Feature Map Selection**: Design quantum feature map for data encoding 2. **Kernel Computation**: Calculate kernel matrix entries via circuit execution 3. **Alignment Optimization**: Tune kernel for target classification task 4. **SVM Training**: Use quantum kernel with classical SVM solvers 5. **Performance Evaluation**: Assess classification accuracy and quantum advantage ## Tools/Libraries - Qiskit Machine Learning - PennyLane - scikit-learn - CVXPY - NumPy--- name: quantum-kernel-estimator-777abf1104b2 description: Quantum kernel computation skill for quantum machine learning allowed-tools: - Bash - Read - Write - Edit - Glob - Grep metadata: specialization: quantum-computing domain: science category: quantum-ml phase: 6 graph: domains: [domain:quantum-computing] specializations: [specialization:quantum-computing] skillAreas: [skill-area:mathematical-reasoning, skill-area:machine-learning-frameworks, skill-area:statistical-analysis] workflows: [workflow:experiment-design] roles: [role:research-scientist, role:ml-engineer] --- # Quantum Kernel Estimator ## Purpose Provides expert guidance on quantum kernel methods for machine learning, enabling kernel-based classifiers and regressors with quantum feature maps. ## Capabilities - Fidelity quantum kernel - Projected quantum kernel - Kernel alignment optimization - Feature map design - SVM integration with quantum kernels - Kernel matrix visualization - Bandwidth tuning - Trainable kernel circuits ## Usage Guidelines 1. **Feature Map Selection**: Design quantum feature map for data encoding 2. **Kernel Computation**: Calculate kernel matrix entries via circuit execution 3. **Alignment Optimization**: Tune kernel for target classification task 4. **SVM Training**: Use quantum kernel with classical SVM solvers 5. **Performance Evaluation**: Assess classification accuracy and quantum advantage ## Tools/Libraries - Qiskit Machine Learning - PennyLane - scikit-learn - CVXPY - NumPy
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.