Entanglement asymmetry, which serves as a diagnostic tool for symmetry breaking and a
proxy for thermalization, has recently been proposed and studied in the context of symmetry …

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

Ground state preparation is classically intractable for general Hamiltonians. On quantum
devices, shallow parametrized circuits can be effectively trained to obtain short-range …

While quantum state tomography is notoriously hard, most states hold little interest to
practically minded tomographers. Given that states and unitaries appearing in nature are of …

Rapid advances in quantum computing technology lead to an increasing need for software
simulators that enable both algorithm design and the validation of results obtained from …

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

Variational quantum algorithm (VQA) derives advantages from its error resilience and high
flexibility in quantum resource requirements, rendering it broadly applicable in the noisy …

Quantum simulation of dynamics in open quantum systems is crucial but poses a significant
challenge due to the non-Hermitian nature leading to nonunitary evolution and the limited …

Quantum computing promises to provide the next step up in computational power for diverse
application areas. In this review, we examine the science behind the quantum hype, and the …

Parameterized quantum circuits (PQCs) have been widely used as a machine learning
model to explore the potential of achieving quantum advantages for various tasks. However …