Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

In scenarios where a large amount of data needs to be learned, incremental learning can
make full use of old knowledge, significantly reduce the computational cost of the overall …

摘要团簇结构可以稳定存在于原子核的内部. 研究原子核的α 团簇结构及其影响在核物理与天体
物理中是一个十分重要的课题. 在过去几十年里, 原子核的团簇结构效应在重离子核反应中有了 …

Quantum machine learning recently gained prominence due to the promise of quantum
computers in solving machine learning problems that are intractable on a classical …

Quantum computers may outperform classical computers on machine learning tasks. In
recent years, a variety of quantum algorithms promising unparalleled potential to enhance …

In the quest to harness the power of quantum computing, training quantum neural networks
(QNNs) presents a formidable challenge. This study introduces an innovative approach …

For the goal of strong artificial intelligence that can mimic human-level intelligence, AI
systems would have the ability to adapt to ever-changing scenarios and learn new …

Neural networks are becoming ubiquitous in various areas of physics as a successful
machine learning (ML) technique for addressing different tasks. Based on ML technique, we …

Catastrophic forgetting emerges when a neural network's parameters undergo continuous
updates during the sequential training of multiple tasks. The ongoing adaptation, while …

Quantum adversarial machine learning is an emerging field that studies the vulnerability of
quantum learning systems against adversarial perturbations and develops possible defense …