Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

Electrochemical and mechanical properties of lithium‐ion battery materials are heavily
dependent on their 3D microstructure characteristics. A quantitative understanding of the …

Strain engineering is demonstrated to effectively regulate the functionality of materials, such
as thermoelectric, ferroelectric, and photovoltaic properties. As the straightforward approach …

The quantum imaginary time evolution is a powerful algorithm for preparing the ground and
thermal states on near-term quantum devices. However, algorithmic errors induced by …

Quantum information processing often requires the preparation of arbitrary quantum states,
such as all the states on the Bloch sphere for two-level systems. While numerical …

The initialization of a quantum system into a certain state is a crucial aspect of quantum
information science. While a variety of measurement strategies have been developed to …

The exotic nature of quantum mechanics differentiates machine learning applications in the
quantum realm from classical ones. Stream learning is a powerful approach that can be …

We present an efficient deep reinforcement learning (DRL) approach to automatically
construct time-dependent optimal control fields that enable desired transitions in dynamical …

We experimentally investigate deep reinforcement learning (DRL) as an artificial intelligence
approach to control a quantum system. We verify that DRL explores fast and robust digital …

In this paper, we present a method to solve the quantum marginal problem for symmetric d-
level systems. The method is built upon an efficient semi-definite program that uses the …