Fig. 4: Performance evaluation of QNLP models for MOF property classification.
From: Property-guided inverse design of metal-organic frameworks using quantum natural language processing
Training dynamics and final accuracies and losses for four QNLP models (the bag-of-words (BoW), DisCoCat, word-sequence with Cups (Cups), and word-sequence model with stairs (Stairs) models) trained on a pore volume and b CO2 Henry’s constant dataset for binary classification task. The left-hand panels display the training losses and accuracies as a function of epochs, showing convergence behavior. The right-hand panels summarize the final training and validation looses (top) and accuracies (bottom) for each model.
