Add support for QuTiP: an open-source software for simulating the dynamics of open quantum systems.

docs/scientific-skills.md

@@ -105,6 +105,7 @@
 - **PyMC** - Comprehensive Python library for Bayesian statistical modeling and probabilistic programming. Provides intuitive syntax for building probabilistic models, advanced MCMC sampling algorithms (NUTS, Metropolis-Hastings, Slice sampling), variational inference methods (ADVI, SVGD), Gaussian processes, time series models (ARIMA, state space models), and model comparison tools (WAIC, LOO). Features include: automatic differentiation via Aesara (formerly Theano), GPU acceleration support, parallel sampling, model diagnostics and convergence checking, and integration with ArviZ for visualization and analysis. Supports hierarchical models, mixture models, survival analysis, and custom distributions. Use cases: Bayesian data analysis, uncertainty quantification, A/B testing, time series forecasting, hierarchical modeling, and probabilistic machine learning
 - **PyMOO** - Python framework for multi-objective optimization using evolutionary algorithms. Provides implementations of state-of-the-art algorithms including NSGA-II, NSGA-III, MOEA/D, SPEA2, and reference-point based methods. Features include: support for constrained and unconstrained optimization, multiple problem types (continuous, discrete, mixed-variable), performance indicators (hypervolume, IGD, GD), visualization tools (Pareto front plots, convergence plots), and parallel evaluation support. Supports custom problem definitions, algorithm configuration, and result analysis. Designed for engineering design, parameter optimization, and any problem requiring optimization of multiple conflicting objectives simultaneously. Use cases: multi-objective optimization problems, Pareto-optimal solution finding, engineering design optimization, and research in evolutionary computation
 - **PyTorch Lightning** - Deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automates training workflows (40+ tasks including epoch/batch iteration, optimizer steps, gradient management, checkpointing), supports multi-GPU/TPU training with DDP/FSDP/DeepSpeed strategies, includes LightningModule for model organization, Trainer for automation, LightningDataModule for data pipelines, callbacks for extensibility, and integrations with TensorBoard, Wandb, MLflow for experiment tracking
+- **QuTiP** - Quantum Toolbox in Python for simulating and analyzing quantum mechanical systems. Provides comprehensive tools for both closed (unitary) and open (dissipative) quantum systems including quantum states (kets, bras, density matrices, Fock states, coherent states), quantum operators (creation/annihilation operators, Pauli matrices, angular momentum operators, quantum gates), time evolution solvers (Schrödinger equation with sesolve, Lindblad master equation with mesolve, quantum trajectories with Monte Carlo mcsolve, Bloch-Redfield brmesolve, Floquet methods for periodic Hamiltonians), analysis tools (expectation values, entropy measures, fidelity, concurrence, correlation functions, steady state calculations), visualization (Bloch sphere with animations, Wigner functions, Q-functions, Fock distributions, matrix histograms), and advanced methods (Hierarchical Equations of Motion for non-Markovian dynamics, permutational invariance for identical particles, stochastic solvers, superoperators). Supports tensor products for composite systems, partial traces, time-dependent Hamiltonians, multiple dissipation channels, and parallel processing. Includes extensive documentation, tutorials, and examples. Use cases: quantum optics simulations (cavity QED, photon statistics), quantum computing (gate operations, circuit dynamics), open quantum systems (decoherence, dissipation), quantum information theory (entanglement dynamics, quantum channels), condensed matter physics (spin chains, many-body systems), and general quantum mechanics research and education
 - **scikit-learn** - Industry-standard Python library for classical machine learning providing comprehensive supervised learning (classification: Logistic Regression, SVM, Decision Trees, Random Forests with 17+ variants, Gradient Boosting with XGBoost-compatible HistGradientBoosting, Naive Bayes, KNN, Neural Networks/MLP; regression: Linear, Ridge, Lasso, ElasticNet, SVR, ensemble methods), unsupervised learning (clustering: K-Means, DBSCAN, HDBSCAN, OPTICS, Agglomerative/Hierarchical, Spectral, Gaussian Mixture Models, BIRCH, MeanShift; dimensionality reduction: PCA, Kernel PCA, t-SNE, Isomap, LLE, NMF, TruncatedSVD, FastICA, LDA; outlier detection: IsolationForest, LocalOutlierFactor, OneClassSVM), data preprocessing (scaling: StandardScaler, MinMaxScaler, RobustScaler; encoding: OneHotEncoder, OrdinalEncoder, LabelEncoder; imputation: SimpleImputer, KNNImputer, IterativeImputer; feature engineering: PolynomialFeatures, KBinsDiscretizer, text vectorization with CountVectorizer/TfidfVectorizer), model evaluation (cross-validation: KFold, StratifiedKFold, TimeSeriesSplit, GroupKFold; hyperparameter tuning: GridSearchCV, RandomizedSearchCV, HalvingGridSearchCV; metrics: 30+ evaluation metrics for classification/regression/clustering including accuracy, precision, recall, F1, ROC-AUC, MSE, R², silhouette score), and Pipeline/ColumnTransformer for production-ready workflows. Features consistent API (fit/predict/transform), extensive documentation, integration with NumPy/pandas/SciPy, joblib persistence, and scikit-learn-compatible ecosystem (XGBoost, LightGBM, CatBoost, imbalanced-learn). Optimized implementations using Cython/OpenMP for performance. Use cases: predictive modeling, customer segmentation, anomaly detection, feature engineering, model selection/validation, text classification, image classification (with feature extraction), time series forecasting (with preprocessing), medical diagnosis, fraud detection, recommendation systems, and any tabular data ML task requiring interpretable models or established algorithms
 - **scikit-survival** - Survival analysis and time-to-event modeling with censored data. Built on scikit-learn, provides Cox proportional hazards models (CoxPHSurvivalAnalysis, CoxnetSurvivalAnalysis with elastic net regularization), ensemble methods (Random Survival Forests, Gradient Boosting), Survival Support Vector Machines (linear and kernel), non-parametric estimators (Kaplan-Meier, Nelson-Aalen), competing risks analysis, and specialized evaluation metrics (concordance index, time-dependent AUC, Brier score). Handles right-censored data, integrates with scikit-learn pipelines, and supports feature selection and hyperparameter tuning via cross-validation
 - **SHAP** - Model interpretability and explainability using Shapley values from game theory. Provides unified approach to explain any ML model with TreeExplainer (fast exact explanations for XGBoost/LightGBM/Random Forest), DeepExplainer (TensorFlow/PyTorch neural networks), KernelExplainer (model-agnostic), and LinearExplainer. Includes comprehensive visualizations (waterfall plots for individual predictions, beeswarm plots for global importance, scatter plots for feature relationships, bar/force/heatmap plots), supports model debugging, fairness analysis, feature engineering guidance, and production deployment