Experimental demonstration of quantum continual learning with superconducting qubits

Quantum computers may outperform classical computers on machine learning tasks. Yet, quantum learning systems may suffer from catastrophic forgetting, which is widely believed to be an obstacle to achieving continual learning. Here, we report an experimental demonstration of quantum continual learning on a superconducting processor. In particular, we sequentially train a quantum classifier with three tasks, two about identifying real-life images and one on classifying quantum states, and demonstrate its catastrophic forgetting. To overcome this dilemma, we exploit the elastic weight consolidation strategy and show that the quantum classifier can incrementally retain knowledge across three tasks with an average accuracy exceeding 92.3%. Additionally, for sequential tasks involving quantum-engineered data, we demonstrate that the quantum classifier outperforms a classical classifier with a comparable number of parameters. Our results establish a viable strategy for empowering quantum learning systems with adaptability to sequential tasks.