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 …

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

We introduce a novel hybrid algorithm to simulate the real-time evolution of quantum
systems using parameterized quantum circuits. The method, named" projected–Variational …

A new paradigm for data science has emerged, with quantum data, quantum models, and
quantum computational devices. This field, called quantum machine learning (QML), aims to …

Simulating the dynamics of large quantum systems is a formidable yet vital pursuit for
obtaining a deeper understanding of quantum mechanical phenomena. While quantum …

The barren plateau phenomenon, characterized by loss gradients that vanish exponentially
with system size, poses a challenge to scaling variational quantum algorithms. Here we …

Variational quantum computing offers a flexible computational paradigm with applications in
diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) …

Quantum computers promise to enhance machine learning for practical applications.
Quantum machine learning for real-world data has to handle extensive amounts of high …

Optimizing parameterized quantum circuits is a key routine in using near-term quantum
devices. However, the existing algorithms for such optimization require an excessive …