A backend-agnostic quantum ML framework over PennyLane and Lightning. It keeps the math separate from the training loop, and runs Monte Carlo barren-plateau detection to tell you whether a circuit is trainable before you spend hours finding out. Still in progress.

The xLSTMTime architecture for long-horizon multivariate forecasting, built from sLSTM and mLSTM blocks with exponential gating. On electricity and traffic data it beats LSTM baselines by 5 to 10% on both MSE and MAE.

Differentiable Tversky similarity as a network layer, from the Tversky Neural Networks paper. It lets a model learn asymmetric similarity instead of assuming a distance metric.