Why is my Qiskit Variational Quantum Regressor (VQR) training taking extremely long with L-BFGS-B and never reaching the evaluation stage?

I'm training a Variational Quantum Regressor (VQR) using Qiskit , and the training is taking an extremely long time. It doesn't appear to crash or throw any errors it just stays in the training phase for a very long time and never reaches the point where it prints the evaluation metrics (MAE, RMSE, R²). My pipeline looks like: Load a CSV dataset for a regression task. Split into training and testing sets. Scale input features using StandardScaler. Scale the target values to [-1, 1] using MinMaxScaler. Create a ZZFeatureMap with: reps = 2 entanglement = "full" Create a RealAmplitudes ansatz with: reps = 2 entanglement = "full" Use L_BFGS_B(maxiter=300) as the optimizer. Construct a VQR and call fit(X_train, y_train_scaled). After training, I predict on the test set and compute MAE, RMSE, and R². Some additional information: Number of features = 8 Number of training samples = 1081 submitted by /u/leeooo_69 [link] [comments]