  Catastrophic forgetting describes the fact that machine learning models will
likely forget the knowledge of previously learned tasks after the learning
process of a new one. It is a vital problem in the continual learning scenario
and recently has attracted tremendous concern across different communities. In
this paper, we explore the catastrophic forgetting phenomena in the context of
quantum machine learning. We find that, similar to those classical learning
models based on neural networks, quantum learning systems likewise suffer from
such forgetting problem in classification tasks emerging from various
application scenes. We show that based on the local geometrical information in
the loss function landscape of the trained model, a uniform strategy can be
adapted to overcome the forgetting problem in the incremental learning setting.
Our results uncover the catastrophic forgetting phenomena in quantum machine
learning and offer a practical method to overcome this problem, which opens a
new avenue for exploring potential quantum advantages towards continual
learning.
