Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA)

Research on Quantum Computing Technology and Application

Mengliang Li, Hong Yang, Xiong Guo

Published Jan 1, 2019• 1 citation
Publisher
DOI
10.2991/masta-19.2019.30

Related papers

Applications of quantum computing technology

Hafiz Md. Hasan Babu

Institute of Physics eBooks•May 1, 2020•2 citations

A survey of quantum computing hybrid applications with brain-computer interface

Dandan Huang, Mei Wang, Jian‐Ping Wang +1 more

In recent years, researchers have paid more attention to the hybrid applications of quantum computing and brain-computer interfaces. With the development of neural technology and artificial intelligence, scientists have become more and more researching brain-computer interface, and the application of brain-computer interface technology to more fields has gradually become the focus of research. While the field of brain-computer interface has evolved rapidly over the past decades, the core technologies and innovative ideas behind seemingly unrelated brain-computer interface systems are rarely summarized from the point of integration with quantum. This paper provides a detailed report on the hybrid applications of quantum computing and brain-computer interface, indicates the current problems, and gives suggestions on the hybrid application research direction.

Cognitive Robotics•Jan 1, 2022•22 citations

A Review on Quantum Computing Approach for Next-Generation Energy Storage Solution

Asadullah Khalid, Shahid Tufail, Arif I. Sarwat

Quantum Computing technology is currently prevailing as a promising candidate for expanding the research horizons in the areas of power engineering and transportation. An across-the-board view of this technology identifies that the intersecting research of interest which covers both areas is the identification of innovative energy storage technologies. Quantum Computers (QC) provide the capability to develop an innovative energy storage system, but its operating principles encompass the domains of Quantum Physics (QP) and Quantum Mechanics (QM) and thus limit the understanding of its underlying functionality. The applicable storage system under research using QC, termed Quantum Battery (QB) is considered to be a theoretical construct, which means that it is not directly observable. This paper attempts to piece together the QM and its applications in a QC, directed towards the identification of an innovative QB, by providing a review of the existing researches. Also, the explanations of various postulates and their corresponding formulations leading to the QM framework behind a QB is presented. This is followed by a review of the underlying operation of a QC and its application in performing battery research. A summary of the existing QB researches is also presented which are categorized into particle- and chemistry-driven (quantum) battery researches.

IEEE SoutheastCon•Mar 10, 2021•3 citations

A Survey on quantum computing technology

László Gyöngyösi, Sándor Imre

Computer Science Review•Jan 27, 2019•646 citations

A Detailed Investigation on Potential Impact of Quantum Computing on Improving Artificial Intelligence

Ganesh Nagaraj, Niraj Upadhayaya, Atheer A. Matroud +3 more

Quantum computing is an emerging technology (QC). Several groups and research institutions are working to make quantum computing applications a reality. Artificial Intelligence (AI) is another emerging field that is becoming more established over time. The main goal of this research is to figure out how the advancement of quantum computing technology will affect AI applications. Computational methods are used to explore the growing influence of quantum computing development for a specific AI application. This study also discusses about the impact and potential of Quantum Computing (QC) on Artificial Intelligence (AI). These can be accomplished by employing cutting-edge methods to deliver usable, economically viable, and scalable technology across a wide range of industries. On the other hand, the risk of committing errors in quantum algorithms, as well as the inability to function without being super-cooled, emerges as a major challenge. Macro-energy systems and the development of sustainable energy materials are just two areas of science and engineering that quantum computing has the potential to completely transform.

International Conference on Innovative Data Communication Technologies and Application (ICIDCA)•Mar 14, 2023•4 citations

Understanding the research and applications of quantum computing

Joshua L. Foss

•Jan 1, 2021•0 citations
In recent years, researchers have paid more attention to the hybrid applications of quantum computing and brain-computer interfaces. With the development of neural technology and artificial intelligence, scientists have become more and more researching brain-computer interface, and the application of brain-computer interface technology to more fields has gradually become the focus of research. While the field of brain-computer interface has evolved rapidly over the past decades, the core technologies and innovative ideas behind seemingly unrelated brain-computer interface systems are rarely summarized from the point of integration with quantum. This paper provides a detailed report on the hybrid applications of quantum computing and brain-computer interface, indicates the current problems, and gives suggestions on the hybrid application research direction.
Quantum Computing technology is currently prevailing as a promising candidate for expanding the research horizons in the areas of power engineering and transportation. An across-the-board view of this technology identifies that the intersecting research of interest which covers both areas is the identification of innovative energy storage technologies. Quantum Computers (QC) provide the capability to develop an innovative energy storage system, but its operating principles encompass the domains of Quantum Physics (QP) and Quantum Mechanics (QM) and thus limit the understanding of its underlying functionality. The applicable storage system under research using QC, termed Quantum Battery (QB) is considered to be a theoretical construct, which means that it is not directly observable. This paper attempts to piece together the QM and its applications in a QC, directed towards the identification of an innovative QB, by providing a review of the existing researches. Also, the explanations of various postulates and their corresponding formulations leading to the QM framework behind a QB is presented. This is followed by a review of the underlying operation of a QC and its application in performing battery research. A summary of the existing QB researches is also presented which are categorized into particle- and chemistry-driven (quantum) battery researches.
Quantum computing is an emerging technology (QC). Several groups and research institutions are working to make quantum computing applications a reality. Artificial Intelligence (AI) is another emerging field that is becoming more established over time. The main goal of this research is to figure out how the advancement of quantum computing technology will affect AI applications. Computational methods are used to explore the growing influence of quantum computing development for a specific AI application. This study also discusses about the impact and potential of Quantum Computing (QC) on Artificial Intelligence (AI). These can be accomplished by employing cutting-edge methods to deliver usable, economically viable, and scalable technology across a wide range of industries. On the other hand, the risk of committing errors in quantum algorithms, as well as the inability to function without being super-cooled, emerges as a major challenge. Macro-energy systems and the development of sustainable energy materials are just two areas of science and engineering that quantum computing has the potential to completely transform.