Advances and Challenges in the Integration of Quantum Computing in Artificial Intelligence: A Systematic Review
Abstract
The main objective of the research is to analyze the incorporation of quantum computing in artificial
intelligence, focusing on applications, tools, and challenges. Through a systematic approach of
literature review in academic databases, a growing interest in this integration was detected,
particularly in fields such as physics, medicine, chemistry, and cybersecurity. It highlights the
effectiveness of quantum algorithms in complex Artificial Intelligence issues such as factorization
and unordered search. Challenges include the vulnerability of qubits and the demand for scalable
quantum hardware. The research recognizes booming tools for quantum data modeling for Artificial
Intelligence, examines innovative applications in materials design and agriculture, and suggests
areas for future research. It highlights the importance of creating more efficient quantum algorithms
and optimizing their integration.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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