Quantum generative modeling is a growing area of interest for industry-relevant
applications. This work systematically compares a broad range of techniques to guide …

Image classification, which is a fundamental element of Computer Vision (CV) and Artificial
Intelligence (AI), has been researched intensively in numerous domains and embedded in …

Tensor networks (TNs) and neural networks (NNs) are two fundamental data modeling
approaches. TNs were introduced to solve the curse of dimensionality in large-scale tensors …

Benchmarking of quantum machine learning (QML) algorithms is challenging due to the
complexity and variability of QML systems, eg, regarding model ansatzes, data sets, training …

The existing body of research on quantum embedding techniques is not only confined in
scope but also lacks a comprehensive understanding of the intricacies of the quantum …

Tensor networks developed in the context of condensed matter physics try to approximate
order-N tensors with a reduced number of degrees of freedom that is only polynomial in N …

The goal of the project Q (AI) 2 was to acquire a broader basis of quantum computing
enhanced AI and optimization algorithms for potential applications in the automotive …

Google's" quantum supremacy" announcement [3] has received broad questions from
academia and industry due to the debatable estimate of 10,000 years' running time for the …

Tensor networks are a popular and computationally efficient approach to simulate general
quantum systems on classical computers and, in a broader sense, a framework for dealing …

量子比特的高效拓展是量子计算获取量子加速优势需要解决的基本问题, 分布式量子计算(DQC)
因其高度可行性和灵活性, 成为解决量子比特拓展问题的关键技术之一. 根据芯片间通信方式的 …