Machine learning improved by quantum computing. Machine learning and quantum physics fix AI and computers. This chapter discusses quantum machine learning theory, methods, and applications. Part 1 thoroughly discusses quantum and classical machine learning. The authors demonstrate how quantum supports vector machines, neural networks, and clustering speed AI. The chapter examines quantum machine learning's merits and downsides. Quantum computers optimize, parallelize, and manage huge data better. Quantum hardware restrictions and error correction reduce noise and decoherence. Explore quantum machine learning in NLP, drug discovery, financial modeling, and image recognition. Many fields could change quantum platform machine learning models with quantum algorithms. The chapter concludes with quantum machine learning directions and challenges. Check trustworthy quantum machine learning frameworks, benchmarks, and hybrid algorithms. Hot: quantum machine learning. This chapter covers fundamentals, research frameworks, and applications.
Top1. Introduction
In the fast-developing subject of quantum computing, computation is done using the concepts of quantum mechanics. It makes use of quantum bits, also known as qubits, which can be in several states at once due to the concept of superposition. As a result, quantum computers can handle enormous volumes of information at once and might even be able to solve some problems more quickly than traditional computers. Entanglement, in which qubits are correlated, and quantum gates, which change qubit states, are important ideas in quantum computing (Adebiyi & Fatinikun et al., 2023).
The goal of artificial intelligence (AI) is to create intelligent machines with capabilities equivalent to those of human beings in terms of perception, learning, reasoning, and decision-making. In order to teach algorithms, which are a subset of AI, how to recognize patterns and make predictions or judgements, massive datasets are used in machine learning. Deep learning, a kind of machine learning, processes complex data and extracts useful features using neural networks with numerous layers. Natural language processing, computer vision, robotics, recommendation systems, and other technologies are just a few of the many uses for AI (Hajimahmud & Khang et al., 2022).
The potential for quantum computing and AI to improve and complement one another is what binds them together. The enormous computational power and simultaneous data processing capacity of quantum computing has the potential to speed up and improve AI algorithms. Quantum algorithms created expressly for AI activities to address optimization issues, better pattern recognition, and improve data processing can use the special characteristics of quantum systems. Moreover, conventional AI algorithms may be improved and quantum-inspired algorithms, which are inspired by quantum principles but can be implemented on classical computers, may overcome some of its drawbacks (Khang & Kali, 2024).
A tool for AI research, quantum computing enables researchers to explore novel architectures, simulate and analyze sophisticated AI models, and create cutting-edge methods that push the field's limits. The relationship between quantum computing and AI is still not fully understood, but it shows promise for addressing difficult issues, enhancing computational effectiveness, and expanding the capabilities of intelligent systems. Unlocking the transformational potential of this connection depends on ongoing research and cooperation between the communities of quantum computing and AI (Khang & Shah et al., 2023).
The domains of machine learning and quantum computing have each made outstanding advancements recently, changing the technological landscape and creating new opportunities for innovation. Using the laws of quantum physics, quantum computing has the potential to tackle complicated problems tenfold more quickly than traditional computers. A revolution in artificial intelligence has been brought about by machine learning, which enables computers to learn from data and make wise predictions or judgements (Khang & Vugar et al., 2023). Now that these two domains have converged, a new field called quantum machine learning (QML) has emerged. QML aims to use quantum computers to improve AI skills and realize the quantum advantage. QML has a lot of potential to revolutionize a number of AI-related fields, including pattern identification, optimization, and data analysis (Metawei et al., 2022). QML algorithms have the potential to surpass their classical counterparts in terms of computational correctness and efficiency by taking advantage of the special characteristics of quantum systems, such as superposition and entanglement. Researchers and industry professionals alike have paid close attention to the interaction between quantum computing and machine learning, which has led to fascinating developments and discoveries (Ushaa, Khang et al., 2023).