Quantum Neural Networks: Bridging Quantum Computing and Machine Learning
Reji Kurien Thomas, Thomas , Reji Kurien
Asian Journal of Research in Computer Science · pp. 1–10 · Published 15 Jun 2023
10.9734/ajrcos/2023/v16i3340Abstract
The usage of QNNs is one approach that might be used to close the knowledge gap that exists between the domains of machine learning and quantum computing. As a result of this research, a full analysis of QNNs has been offered, covering a wide range of issues including their theoretical underpinnings, training techniques, benefits, limits, applications, and assessment methodologies. Quantum neural networks, also known as QNNs, are a type of artificial neural network that makes use of the power of quantum computing. These networks provide the promise for a significant improvement in processing speed as well as greater representation capabilities. On the other hand, in order to make full advantage of the benefits that QNNs give, obstacles such as scalability concerns and hardware restrictions need to be addressed and fixed. The fact that QNNs have the potential to be applied in such a broad range of different sectors is evidence of both the adaptability and the significance of this rapidly developing field of study. It will be necessary to do more study and investigation if there is to be any hope of making headway in the field of quantum machine learning with regard to the creation of QNNs and of realizing their full potential.
References (21)
- 1 Chapter 12: What does the Anthropocene hold for citizenship? [DOI]
- 2 Supervised learning with quantum-enhanced feature spaces [DOI]
- 3 The theory of variational hybrid quantum-classical algorithms [DOI]
- 4 Quantum principal component analysis [DOI]
- 5 Scalable Quantum Simulation of Molecular Energies [DOI]
- 6 Circuit-centric quantum classifiers [DOI]
- 7 GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium [DOI]
- 8 Quantum autoencoders for efficient compression of quantum data [DOI]
- 9 Improved Techniques for Training Single-Image GANs [DOI]
- 10 Genetic algorithms as classical optimizer for the Quantum Approximate Optimization Algorithm [DOI]
- 11 Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry [DOI]
- 12 A non-review of Quantum Machine Learning: trends and explorations [DOI]
- 13 Quantum Natural Language Processing: Challenges and Opportunities [DOI]
- 14 Best practices for portfolio optimization by quantum computing, experimented on real quantum devices [DOI]
- 15 Quantum Machine Learning: Scope for real-world problems [DOI]
- 16 Quantum machine learning [DOI]
- 17 Accelerating Image Classification using Feature Map Similarity in Convolutional Neural Networks [DOI]
- 18 1. Introduction to quantum machine learning [DOI]
- 19 Quantum Reinforcement Learning—Connecting Reinforcement Learning and Quantum Computing [DOI]
- 20 Where is the nucleon spin? [DOI]
- 21 Quantum Approximate Optimization Algorithm (QAOA) [DOI]
Cited by 5
Ankita Nayak, Atmika Patnaik, Ipseeta Satpathy · Advances in Computer and Electrical Engineering · 2024
Nadia Ahmed Sharna, Emamul Islam · Quantum Computing, Communication, and Simulation IV · 2024
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