Over the last two decades, tremendous advances have been made for constructing large-
scale quantum computers. In particular, quantum computing platforms based on …

Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

Quantum error correction is a critical technique for transitioning from noisy intermediate-
scale quantum devices to fully fledged quantum computers. The surface code, which has a …

To ensure a long-term quantum computational advantage, the quantum hardware should be
upgraded to withstand the competition of continuously improved classical algorithms and …

It has been reported that quantum generative adversarial networks have a potential
exponential advantage over classical generative adversarial networks. However, quantum …

We propose a quantum algorithm to solve systems of nonlinear differential equations. Using
a quantum feature map encoding, we define functions as expectation values of parametrized …

Despite its undeniable success, classical machine learning remains a resource-intensive
process. Practical computational efforts for training state-of-the-art models can now only be …

Quantum computing is a game-changing technology for global academia, research centers
and industries including computational science, mathematics, finance, pharmaceutical …

The superiority of variational quantum algorithms (VQAs) such as quantum neural networks
(QNNs) and variational quantum eigensolvers (VQEs) heavily depends on the expressivity …

Generating high-quality data (eg, images or video) is one of the most exciting and
challenging frontiers in unsupervised machine learning. Utilizing quantum computers in …