The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …

A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M) …

Variational quantum algorithms are promising algorithms for achieving quantum advantage
on near-term devices. The quantum hardware is used to implement a variational wave …

In this short review article, we aim to provide physicists not working within the quantum
computing community a hopefully easy-to-read introduction to the state of the art in the field …

Quantum machine learning models have shown successful generalization performance
even when trained with few data. In this work, through systematic randomization …

Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

Variational quantum circuits are used in quantum machine learning and variational quantum
simulation tasks. Designing good variational circuits or predicting how well they perform for …

Nonstabilizerness or magic resource characterizes the amount of non-Clifford operations
needed to prepare quantum states. It is a crucial resource for quantum computing and a …

Quantum neural networks (QNNs) have become an important tool for understanding the
physical world, but their advantages and limitations are not fully understood. Some QNNs …