Quantum minds: Merging quantum computing with next-gen AI
Independent Researcher, USA.
Review Article
World Journal of Advanced Research and Reviews, 2023, 19(03), 1692-1699
Article DOI: 10.30574/wjarr.2023.19.3.1819
Publication history:
Received on 11 July 2023; revised on 12 September 2023; accepted on 14 September 2023
Abstract:
Quantum-enhanced machine learning (QML) is transforming artificial intelligence through the application of quantum computing concepts to solving computationally challenging problems more effectively than conventional methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the capability to speed up deep learning model training, solve combinatorial optimization problems, and improve feature selection in high-dimensional space. It covers basic quantum computer concepts employed within AI, for example, quantum circuits, quantum variational algorithms, and kernel quantum methods, and their impacts on neural networks, generative models, and reinforcement learning.
It further refers to the hybrid quantum-classical architectures in AI where a combination of quantum subroutines and classical deep learning models are employed together with the purpose to gain computational speedup in optimization and handling massive data. Despite the transformative promise of quantum AI, technical issues of qubit noise, error correction, and scaling hardware continue to hold back full implementation. This contribution offers a qualitative overview of quantum-enhanced AI, surveying current applications, research endeavors, and upcoming innovation in quantum deep learning, autonomous systems, and scientific computing. The results open the door for large-scale quantum machine learning architectures, which provide new solutions to future uses of AI in finance, medicine, cyber security, and robotics.
Keywords:
Quantum machine learning; Quantum computing; Artificial intelligence; Quantum neural networks; Quantum kernel methods; Hybrid quantum-classical AI; Variational quantum algorithms; Quantum generative models; Reinforcement learning; Quantum optimization; Quantum advantage; Deep learning; Quantum circuits; Quantum-enhanced AI; Quantum deep learning; Error correction; Quantum-inspired algorithms; Quantum annealing; Probabilistic computing
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