Quantum-Gated Task-interaction Knowledge Distillation for Pre-trained Model-based Class-Incremental Learning

Class-incremental learning (CIL) aims to continuously accumulate knowledge from a stream of tasks and construct a unified classifier over all previously seen classes. A key challenge of CIL lies in the discrepancy between clear task boundaries during training and blurred boundaries during inference, where samples from different tasks often occupy overlapping subspaces. Although pretrained models (PTMs) have shown promising performance in CIL, they still struggle with the entanglement of multi-task subspaces, leading to catastrophic forgetting when task routing parameters are poorly calibrated or task-level representations are rigidly fixed. To address this issue, we propose a novel Quantum-Gated Task-interaction Knowledge Distillation (QKD) framework that leverages quantum gating to guide inter-task knowledge transfer. Specifically, we introduce a quantum-gated task modulation gating mechanism to model the relational dependencies among task embedding, dynamically capturing the sample-to-task relevance for both joint training and inference across streaming tasks. Furthermore, we employ lightweight adapters to adapt PTMs to downstream tasks while freezing previously learned adapters. Guided by the quantum gating outputs, we perform task-interaction knowledge distillation guided by these task-embedding-level correlation weights from old to new adapters, enabling the model to bridge the representation gaps between independent task subspaces and jointly calibrate the unified classifier. Extensive experiments on five benchmark datasets demonstrate that QKD effectively mitigates catastrophic forgetting and achieves state-of-the-art performance in class-incremental settings.