Quantum error mitigation

Z Cai, R Babbush, SC Benjamin, S Endo… - Reviews of Modern …, 2023‏ - APS
For quantum computers to successfully solve real-world problems, it is necessary to tackle
the challenge of noise: the errors that occur in elementary physical components due to …

Quantum simulation for high-energy physics

CW Bauer, Z Davoudi, AB Balantekin, T Bhattacharya… - PRX quantum, 2023‏ - APS
It is for the first time that quantum simulation for high-energy physics (HEP) is studied in the
US decadal particle-physics community planning, and in fact until recently, this was not …

Quantum computing with Qiskit

A Javadi-Abhari, M Treinish, K Krsulich… - arxiv preprint arxiv …, 2024‏ - arxiv.org
We describe Qiskit, a software development kit for quantum information science. We discuss
the key design decisions that have shaped its development, and examine the software …

Brain tumor detection and classification using machine learning: a comprehensive survey

J Amin, M Sharif, A Haldorai, M Yasmin… - Complex & intelligent …, 2022‏ - Springer
Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial
phase, it may lead to death. Despite many significant efforts and promising outcomes in this …

Variational quantum algorithms

M Cerezo, A Arrasmith, R Babbush… - Nature Reviews …, 2021‏ - nature.com
Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …

Exploiting symmetry in variational quantum machine learning

JJ Meyer, M Mularski, E Gil-Fuster, AA Mele, F Arzani… - PRX quantum, 2023‏ - APS
Variational quantum machine learning is an extensively studied application of near-term
quantum computers. The success of variational quantum learning models crucially depends …

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

NR Zhou, TF Zhang, XW **e, 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 …

Neutral atom quantum computing hardware: performance and end-user perspective

K Wintersperger, F Dommert, T Ehmer… - EPJ Quantum …, 2023‏ - epjqt.epj.org
We present an industrial end-user perspective on the current state of quantum computing
hardware for one specific technological approach, the neutral atom platform. Our aim is to …

Effect of data encoding on the expressive power of variational quantum-machine-learning models

M Schuld, R Sweke, JJ Meyer - Physical Review A, 2021‏ - APS
Quantum computers can be used for supervised learning by treating parametrized quantum
circuits as models that map data inputs to predictions. While a lot of work has been done to …

Quantum machine learning: A review and case studies

A Zeguendry, Z Jarir, M Quafafou - Entropy, 2023‏ - mdpi.com
Despite its undeniable success, classical machine learning remains a resource-intensive
process. Practical computational efforts for training state-of-the-art models can now only be …