Quantum Computing in Artificial Intelligence

The goal of quantum computing is to perform tasks with much more precision and efficiency than conventional computers. In the immediate term, quantum computers are unlikely to replace their regular counterparts. To calculate machine learning (ML) algorithms, “Quantum artificial intelligence (Quantum AI)” leverages the processing capability of quantum computing to produce outcomes that are not possible with traditional computers. The technologies of intelligent machines and quantum technology are both revolutionary. Using traditional computers, AI develops practical applications, but it is limited by the computers’ processing power. Quantum computing is based on the nature and behavior of a collection of fundamental particles known as a qubit. The two most fundamental ideas in quantum physics are superposition and entanglement. (1) Superposition: The element enters a superposition state, causing it to act as if it were in each state at the exact same time. (2) Entanglement: Nanoparticles that previously came into contact retain a type of bond and can become twisted in groups, which is known as entanglement. In contrast, quantum computers are designed to handle enormous volumes of data and swiftly identify trends and abnormalities. Using AI and quantum technologies to process massive volumes of data and expedite the resolution of complex algorithms, gain better insight into models, and integrate multiple sets of data. This chapter discusses the benefits of quantum computing, AI, technological challenges, fraud detection, mistake correction, and more.