arXiv:2511.17228v1 Announce Type: cross 
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.

أظهرت التطورات الأخيرة في نماذج التعلم الكمومي قدرتها على الحفاظ على اللدونة في بيئات التعلم المستمر، مما يعالج قيدًا كبيرًا موجودًا في أنظمة التعلم العميق التقليدية. تظهر هذه النماذج قدرات تعلم متسقة عبر مجموعة متنوعة من المهام وأنواع البيانات، بما في ذلك التعلم المراقب والتعلم المعزز.

Los avances recientes en modelos de aprendizaje cuántico han demostrado su capacidad para mantener la plasticidad en entornos de aprendizaje continuo, abordando una limitación significativa que se encuentra en los sistemas de aprendizaje profundo tradicionales. Estos modelos muestran capacidades de aprendizaje consistentes en diversas tareas y tipos de datos, incluidos el aprendizaje supervisado y el aprendizaje por refuerzo.

Des avancées récentes dans les modèles d'apprentissage quantique ont démontré leur capacité à maintenir la plasticité dans des environnements d'apprentissage continu, répondant à une limitation significative des systèmes d'apprentissage profond traditionnels. Ces modèles montrent des capacités d'apprentissage cohérentes à travers diverses tâches et types de données, y compris l'apprentissage supervisé et l'apprentissage par renforcement.

Recent advancements in quantum learning models have demonstrated their ability to maintain plasticity in continual learning environments, addressing a significant limitation found in traditional deep learning systems. These models show consistent learning capabilities across various tasks and data types, including supervised and reinforcement learning.

Intrinsic preservation of plasticity in continual quantum learning

Artificial intelligence is fascinating, transformative and increasingly woven into how we learn, work and make decisions.

يبرز النقاش المستمر حول الذكاء الاصطناعي (AI) الحاجة إلى إنشاء أطر تنظيمية لضمان دمجه بشكل آمن في المجتمع. بينما يُعترف بالذكاء الاصطناعي لإمكاناته التحويلية في مختلف القطاعات، فإن المخاوف بشأن تطويره غير المنضبط تتطلب اتخاذ تدابير استباقية لمنع النتائج السلبية.

El discurso en torno a la inteligencia artificial (IA) enfatiza la necesidad de establecer marcos regulatorios para garantizar su integración segura en la sociedad. Si bien se reconoce que la IA tiene un potencial transformador en varios sectores, las preocupaciones sobre su desarrollo descontrolado requieren medidas proactivas para prevenir resultados adversos.

Le discours actuel sur l'intelligence artificielle (IA) souligne la nécessité d'établir des cadres réglementaires pour garantir son intégration sécurisée dans la société. Bien que l'IA soit reconnue pour son potentiel transformateur dans divers secteurs, des préoccupations concernant son développement incontrôlé nécessitent des mesures proactives pour prévenir des résultats indésirables.

The ongoing discourse around artificial intelligence (AI) emphasizes the need for establishing regulatory frameworks to ensure its safe integration into society. While AI is recognized for its transformative potential in various sectors, concerns about its unchecked development necessitate proactive measures to prevent adverse outcomes.

Intrinsic preservation of plasticity in continual quantum learning

Was this article worth reading? Share it

Invent

IntelliQ

Cogent