The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …

Generative adversarial networks (GANs) are one of the most widely adopted machine
learning methods for data generation. In this work, we propose a new type of architecture for …

Deep generative models are key-enabling technology to computer vision, text generation,
and large language models. Denoising diffusion probabilistic models (DDPMs) have …

Simulating response properties of molecules is crucial for interpreting experimental
spectroscopies and accelerating materials design. However, it remains a long-standing …

In the modern financial industry system, the structure of products has become more and
more complex, and the bottleneck constraint of classical computing power has already …

As a generalized quantum machine learning model, parameterized quantum circuits (PQC)
have been found to perform poorly in terms of classification accuracy and model scalability …

Quantum metrology aims to measure physical quantities based on fundamental quantum
principles, enhancing measurement precision through resources like quantum …

Quantum machine learning and vision have come to the fore recently, with hardware
advances enabling rapid advancement in the capabilities of quantum machines. Recently …

As a branch of machine learning, generative models are widely used in supervised and
unsupervised learning. To speedup certain machine learning tasks, quantum generative …

Quantum generative adversarial networks (QGANs), an intersection of quantum computing
and machine learning, have attracted widespread attention due to their potential advantages …