Noisy intermediate-scale quantum algorithms

K Bharti, A Cervera-Lierta, TH Kyaw, T Haug… - Reviews of Modern …, 2022 - APS
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 …

Superconducting quantum computing: a review

HL Huang, D Wu, D Fan, X Zhu - Science China Information Sciences, 2020 - Springer
Over the last two decades, tremendous advances have been made for constructing large-
scale quantum computers. In particular, quantum computing platforms based on …

Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution

NR Zhou, TF Zhang, XW Xie, JY Wu - Signal Processing: Image …, 2023 - Elsevier
It has been reported that quantum generative adversarial networks have a potential
exponential advantage over classical generative adversarial networks. However, quantum …

Parameterized quantum circuits as machine learning models

M Benedetti, E Lloyd, S Sack… - Quantum Science and …, 2019 - iopscience.iop.org
Hybrid quantum–classical systems make it possible to utilize existing quantum computers to
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …

Noisy intermediate-scale quantum computers

B Cheng, XH Deng, X Gu, Y He, G Hu, P Huang, J Li… - Frontiers of …, 2023 - Springer
Quantum computers have made extraordinary progress over the past decade, and
significant milestones have been achieved along the path of pursuing universal fault-tolerant …

Experimental quantum generative adversarial networks for image generation

HL Huang, Y Du, M Gong, Y Zhao, Y Wu, C Wang… - Physical Review …, 2021 - APS
Quantum machine learning is expected to be one of the first practical applications of near-
term quantum devices. Pioneer theoretical works suggest that quantum generative …

Machine learning for quantum matter

J Carrasquilla - Advances in Physics: X, 2020 - Taylor & Francis
Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

Quantum machine learning for chemistry and physics

M Sajjan, J Li, R Selvarajan, SH Sureshbabu… - Chemical Society …, 2022 - pubs.rsc.org
Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

Quantum state tomography with conditional generative adversarial networks

S Ahmed, C Sánchez Muñoz, F Nori, AF Kockum - Physical review letters, 2021 - APS
Quantum state tomography (QST) is a challenging task in intermediate-scale quantum
devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the …

Hybrid quantum-classical convolutional neural networks

J Liu, KH Lim, KL Wood, W Huang, C Guo… - Science China Physics …, 2021 - Springer
Deep learning has been shown to be able to recognize data patterns better than humans in
specific circumstances or contexts. In parallel, quantum computing has demonstrated to be …