Machine learning meets quantum

Pengfei Zhang

P Kuppusamy, Nnvs Yaswanth Kumar, Jyotsna Dontireddy +1 more

The rapid development of machine learning technology leads to make the devices in the industries working autonomously. However, the growth of sensors in the industries leads to produce vast data that is utilized by machine learning algorithms to improve the autonomous devices’ performances. However, classical ML algorithms and hardware systems cannot process large data to meet real-time problems. Hence, the researchers have developed Quantum Computing hardware systems and Quantum Machine learning algorithms to speed up the process. This research work presented the review of quantum computing mechanisms and QML algorithms that are applied to classify the images. This work demonstrated the performance comparison of various QML algorithms. It showed that the images are classified using various QML algorithms faster than classical ML algorithms in terms of time.

Chenfeng Cao

HKUST Electronic Theses Quantum information meets variational learning by Chenfeng Cao thesis 2023 1 online resource (xviii, 158 pages) : illustrations (some color) This thesis explores the intersection of quantum information and variational learning, focusing on classical machine learning for…Read more ›

Cristian Romero García

Kishor Bharti, Tobias Haug, Vlatko Vedral +1 more

The goal of machine learning is to facilitate a computer to execute a specific task without explicit instruction by an external party. Quantum foundations seek to explain the conceptual and mathematical edifice of quantum theory. Recently, ideas from machine learning have successfully been applied to different problems in quantum foundations. Here, the authors compile the representative works done so far at the interface of machine learning and quantum foundations. The authors conclude the survey with potential future directions.