About this Research Topic

Background

The research on quantum artificial intelligence is at the intersection of quantum information science (QIS), artificial intelligence, soft computing, computational intelligence, machine learning, deep learning, optimization, etc. It touches on many important parts of near-term quantum computing and noisy intermediate-scale quantum (NISQ) devices. The research on quantum artificial intelligence is grounded in theories, modelling, and significant studies on hybrid classical-quantum algorithms using classical simulations, IBM Q services, PennyLane, Google Cirq, D-Wave quantum annealer etc. So far, the research on quantum artificial intelligence has given us the building blocks to achieve quantum advantage to solve problems in combinatorial optimization, soft computing, deep learning, and machine learning much faster than traditional classical computing. Solving these problems is important for making quantum computing useful for noise-resistant large-scale applications. This makes it much easier to see the big picture and helps with cutting-edge research across the quantum stack, making it an important part of any QIS effort.

Quantum technologies have reached a point where their broad implementation requires the participation of several disciplines. The objective of this QAI is to investigate the potential uses of artificial intelligence and related technologies in quantum applications and to educate the computational intelligence community about current advances in quantum information technology. Numerous quantum information and processing systems have been created and proven in labs, fields, and commercial settings during the last few decades. Results demonstrate the viability of real-world applications in sectors relevant to quantum artificial intelligence. This encompasses data security, optimization, finance, high-precision sensors, simulations, and computer applications. Quantum technologies have received considerable support for research and development from corporations and governments. However, considerable work is required to bring quantum technology-based gadgets and systems to consumers' homes. In addition, many challenges provide chances to contribute knowledge, technology, and engineering from outside the field of artificial intelligence. The purpose of this research topic is to bring together individuals from academia and industry, from the classical and quantum artificial intelligence communities, to discuss the theory, technology, and applications of quantum technologies, and to exchange ideas on how to efficiently advance the engineering and development of this fascinating field.

In recent years, we have seen research being conducted to improve artificially intelligent systems based on quantum computing ideas. This emerging field of quantum artificial intelligence research focuses on the study of quantum computing, which is characterized by certain principles of quantum mechanics such as standing waves, interference, quantum bits, coherence, superposition of states, and the interference concept, combined with machine learning, computational intelligence, and soft computing approaches such as artificial neural networks, fuzzy systems, evolutionary computing, swarm intelligence, and hybrid soft computing methods. This research area presents a broad range of research projects that integrate artificial intelligence with quantum computing systems. Quantum Artificial Intelligence (QAI) is a high-impact, worldwide publication that publishes experimental and theoretical research of the highest calibre in all fields of quantum computational intelligence. Quantum soft computing, quantum machine learning, quantum-inspired soft computing, hybrid classical-quantum neural network models, qubit- and qutrit-based quantum-inspired neural network models, quantum optimization, hybrid classical-quantum algorithms (variational quantum algorithms), etc., are of particular interest. QAI encourages and facilitates research by offering a thorough peer review process and distributing high-quality findings in a variety of forms. These comprise original regular papers, short papers, letters, detailed review articles, and special issue topics. Research areas relevant to quantum artificial intelligence include but are not limited to the following topics:

1. Quantum machine learning,
2. Quantum-inspired soft computing,
3. Hybrid classical-quantum neural network models,
4. Qubit- and qutrit-based quantum-inspired neural network models,
5. Quantum optimization,
6. Hybrid classical-quantum algorithms
7. Variational quantum algorithms
8. Quantum metaheuristics

Keywords: Quantum Computing, Quantum Machine Learning, Quantum Machine Intelligence, Quantum Computational Intelligence, Hybrid Quantum-Classical Algorithm, Quantum Optimization Algorithms, Quantum Algorithms, Quantum-inspired Soft Computing

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