Abstract

Next-generation wireless communication systems should have very low latency, high reliability, the ability to connect a lot of devices, and better security. Artificial intelligence (AI) is an important part of intelligent network management because it makes channel estimation, spectrum allocation, beamforming, and network optimization more efficient. But classical AI methods have a lot of problems when used on large wireless networks, such as being very hard to compute, using a lot of energy, and not being able to grow. Recently quantum artificial intelligence i.e.QAI which combines quantum computing with machine learning has become a promising way to get around these problems. By using quantum mechanics ideas like superposition, entanglement and quantum parallelism QAI could speed up optimization tasks, make pattern recognition better and make secure communication better. This paper gives a full overview of QAI and how it could be used in the next generation of wireless communication systems. The study talks about the basics of quantum computing, how AI can help wireless networks, and new quantum machine learning models that are being developed. Also, quantum algorithms like Grover's search algorithm and the Quantum Approximate Optimization Algorithm (QAOA) are looked at in relation to problems with optimizing wireless networks. Lastly, we talk about the problems that QAI-enabled wireless communication systems face right now and the research that needs to be done in the future.

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