Quantum Artificial Intelligence (Quantum AI) suits quantum computing and
artificial intelligence to tackle complex issues such as multi-dimensional optimization
and massive volumes of information more proficiently than classic strategies. Quantum
AI utilizes quantum phenomena like superposition, entanglement, and tunneling,
among others, to realize a speedup of several orders of magnitude compared to
classical AI algorithms. Quantum AI is capable of being implemented for healthcare,
finance, and cybersecurity. Whereas quantum simulations are used for accelerating
drug discovery and for improving the accuracy of molecular modeling, they also
benefit precision medicine through advanced genomic analysis as well as diagnostics,
including precision diagnostics, through the precision testing of natural disease
biomarkers, active and inactive metabolites, and toxic blood chemistry. Quantum AI in
finance enhances trading profitability by accelerating the prediction of real-time market
trends, optimization of the portfolio, and financial fraud detection, while processing
vast datasets. Quantum cryptography, e.g., quantum key distribution, provides security
in cybersecurity, whereas quantum algorithms, such as Shor, bring threats to
encryption, and thus post-quantum cryptography is developed. This chapter shows that
Quantum AI pertains to practical feasibility, despite error correction, coherence times,
and a lack of algorithm development. What we show is about how Quantum AI can
transform these sectors, what the stumbling blocks are, hardware scalability, and
algorithm efficiency, in addition to quantum computing reliability. This study,
therefore, highlights the importance of continuing to invest in Quantum AI so that it
can be fully realized.
Keywords: AI ethics, Cloud AI, Cybersecurity, Economics, Entanglement, Finance, Frameworks, Google quantum, Healthcare, Hybrid computing, Intelligent computing, Quantum computing, Quantum machine learning, Quantum mechanics, Quantum AI.
