Transformer architectures have facilitated the development of large-scale and general-
purpose sequence models for prediction tasks in natural language processing and computer …

A core technology that has emerged from the artificial intelligence revolution is the recurrent
neural network (RNN). Its unique sequence-based architecture provides a tractable …

Recently developed methods in computational neuroethology have enabled increasingly
detailed and comprehensive quantification of animal movements and behavioral kinematics …

Recurrent neural networks (RNNs), originally developed for natural language processing,
hold great promise for accurately describing strongly correlated quantum many-body …

With rapid progress across platforms for quantum systems, the problem of many-body
quantum state reconstruction for noisy quantum states becomes an important challenge …

During a quantum measurement, superpositions of states with different observable
properties probabilistically collapse into one with a sharp value of the measured observable …

Generative modeling has seen a rising interest in both classical and quantum machine
learning, and it represents a promising candidate to obtain a practical quantum advantage in …

A few models have tried to tackle the link prediction problem, also known as knowledge
graph completion, by embedding knowledge graphs in comparably lower dimensions …

The quantum neural network is one of the promising applications for near-term noisy
intermediate-scale quantum computers. A quantum neural network distills the information …

Rydberg atom array experiments have demonstrated the ability to act as powerful quantum
simulators, preparing strongly-correlated phases of matter which are challenging to study for …