Quantum Computing and AI can be integrated in a manner that will boost
machine learning by eliminating obstacles presented by ordinary systems. This chapter
delves into the relationship between Quantum Computing and Machine Learning,
offering a critical examination of how quantum mechanics boosts conventional AI
algorithms. The chapter starts by defining the basics of Quantum Computing, i.e.,
qubits, superposition, entanglement, and quantum gates, and then proceeds to cover the
essential principles of Quantum Machine Learning (QML) algorithms like Quantum
Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Quantum
Principal Component Analysis (QPCA). The chapter further gives some more details
about Quantum-Driven AI and where its applications could be (in the medical sector, in
finance, in cyber security, and in optimization problems). The advantages of Quantum
AI have been discussed earlier, but it has some disadvantages as well, such as a lack of
hardware, algorithmic complexities, and even issues regarding the morality of the
technology. Lastly, the possibility of Quantum AI and the evolutionary strategy to
bring this technology are emphasized. Now, with greater advancements in the physical
substrates and new quantum peripherals in the quantum computer, more advanced
construction of hybrid quantum-classical models, and more engagement of artificial
intelligence in quantum research. People in all fields anticipate quantum mechanics and
artificial intelligence to innovate and rejuvenate the industries and sectors they have
adopted. This groundbreaking chapter will summarize the nature of Quantum AI to the
researchers, experts, and anybody who wishes to venture into the world's next big
thing.
Keywords: Artificial neural networks (ANNs), Boltzmann machines, Clustering algorithms, Deep learning, Grover’s algorithm, Hybrid quantum-classical models, Noisy intermediate-scale quantum (NISQ), Quantum approximate optimization algorithm (QAOA), Quantum boltzmann machines, Quantum data processing, Quantum feature mapping, Quantum linear regression, Quantum principal component analysis (QPCA), Quantum reinforcement learning (QRL).
