On the one hand, we have machine learning; on the other hand, we face questions about learning at the quantum level. Can we develop AI for quantum computers? Do we need to understand the role of intuition in the brain to enable the emergence of a Human Artificial Intelligence (HAI)? Vision operates at the speed of light, while a quantum event occurs faster than thought, at infinite speed. Yet, this event is slowed down as a sensation, allowing it to be interpreted into icons and language by thought. Sensation, however, due to the intermediary of language, can never directly become thought. (This is discussed more fully in the section “Quantum Conceptualised.”)
On the one hand, we have machine learning; on the other hand, we face questions about learning at the quantum level. Can we develop AI for quantum computers? Do we need to understand the role of intuition in the brain to enable the emergence of a Human Artificial Intelligence (HAI)? Vision operates at the speed of light, while a quantum event occurs faster than thought, at infinite speed. Yet, this event is slowed down as a sensation, allowing it to be interpreted into icons and language by thought. Sensation, however, due to the intermediary of language, can never directly become thought. (This is discussed more fully in the section “Quantum Conceptualised.”)
Emerging technologies, including quantum information science and artificial education systems, have the potential to have significant implications for the future of human civilization. Quantum information, on the one hand, and machine learning (ML) and artificial intelligence (AI), on the other, consume their personal unique set of queries and contests that have been studied in isolation up until now. However, a recent study is starting to examine whether these disciplines can teach one another anything useful. The discipline of quantum ML investigates how quantum computing and ML may work together to find solutions to challenges in both areas. Major advancements in the two areas of effect have been made recently. Particularly relevant in today's “big data” era is the use of quantum computing to speed up the solution of machine learning (ML) challenges. However, ML is already present in many state-of-the-art technologies and may play a crucial role in future quantum technologies.
Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain.
Artificial intelligence (AI) technologies are attractive and growing universal and extensive in a broad range of perspectives from commercial applications to cutting-edge scientific research. In AI, one can simulate human intelligence processes via machines, especially computer systems. In a true sense, AI is a part of our modern life, as it is quite tough to identify an area where accomplishments in the field of AI would not be used. AI technologies enable machines to learn useful processes, including object recognition, speech recognition, natural language processing, machine vision, and digital personal assistants. In this context, quantum optics and quantum-based technologies have been found in prominent positions since they enable a speed up in learning processes [ 1–4 ]. It has been perceived that the demand for the algorithms that enable machines to learn quickly and efficiently have increased day-by-day as the field of AI has advanced. Quantum machine learning is a fast rising field connecting AI and quantum technology.
Quantum computing has transformed into a revolutionary technology which can revolutionize artificial intelligence (AI) and machine learning (ML). The processing capabilities of quantum systems rely on the principles of superposition and entanglement to complete operations at quantum-fast speeds which drives solutions for optimization problems and pattern detection along with deep learning breakthroughs. The document analyzes quantum computing fundamentals and its leadership over conventional systems and their applications toward enhancing AI and ML capabilities. This paper examines modern advancements as well as present obstacles and future prospects of this fast-growing field.
Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent these fields can learn and benefit from each other. QML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently, we have witnessed breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world. Conversely, ML already permeates cutting-edge technologies, and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been demonstrated for interactive learning, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments, and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement, researchers have also broached the fundamental issue of quantum generalizations of ML/AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is described by quantum mechanics. In this review, we describe the main ideas, recent developments, and progress in a broad spectrum of research investigating machine learning and artificial intelligence in the quantum domain.
Artificial Intelligence (AI), a discipline with decades of history, is living its golden era due to striking developments that solve problems that were unthinkable just a few years ago, like generative models of text, images and video.The broad range of AI applications has also arrived to Physics, providing solutions to bottleneck situations, e.g., numerical methods that could not solve certain problems or took an extremely long time, optimization of quantum experimentation, or qubit control.Besides, Quantum Computing has become extremely popular for speeding up AI calculations, especially in the case of data-driven AI, i.e., Machine Learning (ML).The term Quantum ML is already known and deals with learning in quantum computers or quantum annealers, quantum versions of classical ML models and different learning approaches for quantum measurement and control.Quantum AI (QAI) tries to take a step forward in order to come up with disruptive concepts, such as, human-quantum-computer interfaces, sentiment analysis in quantum computers or explainability of quantum computing calculations, to name a few.This special session includes five high-quality papers on relevant topics, like quantum reinforcement learning, parallelization of quantum calculations, quantum feature selection and quantum vector quantization, thus capturing the richness and variability of approaches within QAI.