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

Quantum tangent kernel methods provide an efficient approach to analyzing the
performance of quantum machine learning models in the infinite-width limit, which is of …

Variational quantum algorithms, inspired by neural networks, have become a novel
approach in quantum computing. However, designing efficient parameterized quantum …

In this paper, the application of quantum simulations and quantum machine learning is
explored to solve problems in low‐energy nuclear physics. The use of quantum computing …

Quantum key distribution (QKD) is an encrypted communication technique based on the
principles of quantum mechanics that ensures communication security by exploiting the …

Variational quantum algorithms is one of the most representative algorithms in quantum
computing, which has a wide range of applications in quantum machine learning, quantum …

Mixers play a crucial role in superconducting quantum computing, primarily by facilitating
frequency conversion of signals to enable precise control and readout of quantum states …

Understanding the power of parameterized quantum circuits (PQCs) in accomplishing
machine learning tasks is one of the most important questions in quantum machine learning …

Kernel function plays a crucial role in machine learning algorithms such as classifiers. In this
paper, we aim to improve the classification performance and reduce the reading out burden …

Machine learning has demonstrated tremendous potential in solving real-world problems.
However, with the exponential growth of data amount and the increase of model complexity …