Quantum computing models

Sergei Kurgalin, Sergei Borzunov

N. A. Natraj, Pethuru Raj, J. Arockia Venice +2 more

Quantum generative adversarial networks (QGANs) is a novel topical approach that enhances data creation and analysis by incorporating elements of quantum computing quantum computing (QC) into GANs. This chapter aims to look at the relatively young research area called QGANs, which combines the cutting-edge technology of quantum computing quantum computing (QC) with traditional generative modeling to reveal its potential in various fields. Thus, QGANs are expected to have the most profound impact on quantum chemistry, financial approaches, image processing, and many other fields, as they implement the quantum perspective on data generation by utilizing the potential of quantum structures. Some are asymptotic improvements, and others are algorithmic improvements for better stability and scalability using quantum hardware, and finding use in quantum computing quantum computing (QC) applications in various fields, such as pharmaceuticals or climate science. The advancement of QGAN can only be attributed to the advancement in quantum computing quantum computing (QC) hardware and the better ways of implementing both classical and quantum paradigms for enhancing performance. As with other AI technolog AI technology ies, there are significant ethical concerns over data privacy protection and possible bias within the generated data, not to mention relevant legislation of practical QGAN uses. Nevertheless, the described challenges do not undermine the benefits that QGANs may offer in the future to address problems that classical computing cannot solve on its own, thus opening a new era in data modeling. As further research and development advance the functions and functionality of QGANs, the readiness of QGANs for broader practical use is evident, thus opening up a transformative and luxurious era in the intersecting areas of quantum computing quantum computing (QC) and generative model generative model s.

Peter P. Rohde

Jeongho Bang, Junghee Ryu, Chang‐Woo Lee +3 more

A model of quantum computation (QC) is compared with a generalized classical computation model, named stochastic ensemble computation (SEnC), in article number 1800037 by Jeongho Bang, Jinhyoung Lee, Wonmin Son and co-workers. SEnC runs in an ensemble consisting of finite copies of single probabilistic entities, and hence is a generalized probabilistic Turing machine. Impossibility of simulating a QC in the SEnC is proved, manifesting “QC is characterized beyond any classical probabilistic computation,” i.e., SEnC ⊆ QC.

Kimaya Rajput, Stephanie Callahan

Akshay Ajagekar, Fengqi You

Quantum computing and deep learning methods hold great promise to open up a new era of computing and have been receiving significant attention recently. This paper presents quantum computing (QC) based deep learning methods for fault diagnosis that are capable of overcoming the computational challenges faced by conventional techniques performed on classical computers. The shortcomings of such classical data-driven techniques are addressed by the proposed QC-based fault diagnosis model. A quantum computing assisted generative training process followed by supervised discriminative training is used to train this model. The applicability of proposed model and methods is demonstrated by applying them to process monitoring of Tennessee Eastman (TE) process. The proposed QC-based deep learning approach enjoys superior performance with an average fault diagnosis rate of 80% and tremendously low false alarm rates for the TE process.