Intrinsic preservation of plasticity in continual quantum learning

TL;DR

Quantum learning models inherently preserve plasticity over time, overcoming a key limitation of classical deep learning, by leveraging their intrinsic physical constraints to maintain consistent learning capabilities across diverse tasks and data modalities.

Contribution

This paper demonstrates that quantum neural networks naturally retain plasticity in continual learning scenarios, unlike classical models, due to their unitary constraints which prevent unbounded weight growth.

Findings

Quantum models maintain stable learning over long timescales.

Classical models show performance degradation with unbounded weight growth.

Quantum models outperform classical ones across various tasks and data types.

Abstract

Artificial intelligence in dynamic, real-world environments requires the capacity for continual learning. However, standard deep learning suffers from a fundamental issue: loss of plasticity, in which networks gradually lose their ability to learn from new data. Here we show that quantum learning models naturally overcome this limitation, preserving plasticity over long timescales. We demonstrate this advantage systematically across a broad spectrum of tasks from multiple learning paradigms, including supervised learning and reinforcement learning, and diverse data modalities, from classical high-dimensional images to quantum-native datasets. Although classical models exhibit performance degradation correlated with unbounded weight and gradient growth, quantum neural networks maintain consistent learning capabilities regardless of the data or task. We identify the origin of the advantage as the intrinsic physical constraints of quantum models. Unlike classical networks where unbounded weight growth leads to landscape ruggedness or saturation, the unitary constraints confine the optimization to a compact manifold. Our results suggest that the utility of quantum computing in machine learning extends beyond potential speedups, offering a robust pathway for building adaptive artificial intelligence and lifelong learners.