Trainability

In quantum computing, *trainability* refers to how effectively a quantum machine learning model (like a variational quantum circuit) can be optimized to minimize its loss function. It depends on whether small parameter changes produce meaningful gradients rather than vanishing ones. Poor trainability—often caused by issues like barren plateaus—means the model can’t learn efficiently because gradients become too small to guide improvement.