Noisy intermediate-scale quantum computers
Quantum computers have made extraordinary progress over the past decade, and
significant milestones have been achieved along the path of pursuing universal fault-tolerant …
significant milestones have been achieved along the path of pursuing universal fault-tolerant …
Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
A quantum convolutional neural network on NISQ devices
Quantum machine learning is one of the most promising applications of quantum computing
in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional …
in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional …
Review of quantum image processing
Z Wang, M Xu, Y Zhang - Archives of Computational Methods in …, 2022 - Springer
As an interdisciplinary between quantum computing and image processing, quantum image
processing provides more possibilities for image processing due to the powerful parallel …
processing provides more possibilities for image processing due to the powerful parallel …
Contemporary quantum computing use cases: taxonomy, review and challenges
Recently, the popularity of using the expressive power of quantum computing to solve
known, challenging problems has increased remarkably. This study aims to develop a clear …
known, challenging problems has increased remarkably. This study aims to develop a clear …
Quantum adversarial machine learning
Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of
machine learning approaches in adversarial settings and develo** techniques …
machine learning approaches in adversarial settings and develo** techniques …
Image classification based on quantum K-Nearest-Neighbor algorithm
Y Dang, N Jiang, H Hu, Z Ji, W Zhang - Quantum Information Processing, 2018 - Springer
Image classification is an important task in the field of machine learning and image
processing. However, common classification method, the K-Nearest-Neighbor algorithm …
processing. However, common classification method, the K-Nearest-Neighbor algorithm …
Circuit-based quantum random access memory for classical data
A prerequisite for many quantum information processing tasks to truly surpass classical
approaches is an efficient procedure to encode classical data in quantum superposition …
approaches is an efficient procedure to encode classical data in quantum superposition …
An improved novel quantum image representation and its experimental test on IBM quantum experience
J Su, X Guo, C Liu, S Lu, L Li - Scientific Reports, 2021 - nature.com
Quantum image representation (QIR) is a necessary part of quantum image processing
(QIP) and plays an important role in quantum information processing. To address the …
(QIP) and plays an important role in quantum information processing. To address the …
Time-series quantum reservoir computing with weak and projective measurements
Time-series processing is a major challenge in machine learning with enormous progress in
the last years in tasks such as speech recognition and chaotic series prediction. A promising …
the last years in tasks such as speech recognition and chaotic series prediction. A promising …