Quantum computing and artificial intelligence are two highly topical fields of research that can benefit from each other’s discoveries by opening a completely new scenario in computation, that of quantum artificial intelligence. Indeed, on the one hand, artificial intelligence algorithms can be made computationally more efficient due to the potential speedup enabled by quantum phenomena; on the other hand, the complex development of quantum computing technologies and methodologies can be properly supported by the use of classical artificial intelligence approaches. The “entanglement” of these two disciplines is opening up completely new directions in computer science research, and this survey aims to provide a systematic and taxonomic overview of the work that has already been done and that which will begin in the near future.
Quantum computing and artificial intelligence are two highly topical fields of research that can benefit from each other’s discoveries by opening a completely new scenario in computation, that of quantum artificial intelligence. Indeed, on the one hand, artificial intelligence algorithms can be made computationally more efficient due to the potential speedup enabled by quantum phenomena; on the other hand, the complex development of quantum computing technologies and methodologies can be properly supported by the use of classical artificial intelligence approaches. The “entanglement” of these two disciplines is opening up completely new directions in computer science research, and this survey aims to provide a systematic and taxonomic overview of the work that has already been done and that which will begin in the near future.
Quantum computing and artificial intelligence (AI) are two of the most transformative technologies of the 21st century. The convergence of these fields promises unprecedented advancements in computational capabilities, enabling solutions to complex problems that are currently intractable for classical computers. This paper explores the intersection of quantum computing and AI, examining the potential impacts, current research, challenges, and future directions. By leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism, quantum computing can enhance AI algorithms, offering exponential speedups and new methodologies for machine learning, optimization, and data analysis.
Abstract Quantum Artificial Intelligence (QAI) is the intersection of quantum computing and AI, a technological synergy with expected significant benefits for both. In this paper, we provide a brief overview of what has been achieved in QAI so far and point to some open questions for future research. In particular, we summarize some major key findings on the feasability and the potential of using quantum computing for solving computationally hard problems in various subfields of AI, and vice versa, the leveraging of AI methods for building and operating quantum computing devices.
Quantum Artificial Intelligence (QAI) is the intersection of quantum computing and AI, a technological synergy with expected significant benefits for both. In this paper, we provide a brief overview of what has been achieved in QAI so far and point to some open questions for future research. In particular, we summarize some major key findings on the feasability and the potential of using quantum computing for solving computationally hard problems in various subfields of AI, and vice versa, the leveraging of AI methods for building and operating quantum computing devices.
Quantum computing, with its unique properties and potential to solve complex problems more efficiently than classical computers, has emerged as a promising field in the realm of computational science. The intersection of quantum computing and artificial intelligence (AI) presents a fertile ground for innovation, particularly in the development of quantum algorithms that can enhance machine learning models. This paper explores the theoretical foundations, current research, and potential applications of quantum computing in AI. We delve into the mathematical underpinnings of quantum algorithms, their integration with machine learning models, and the practical challenges and opportunities that lie ahead. Through a detailed analysis of existing research and the presentation of novel algorithms, this paper aims to provide a comprehensive overview of the current state and future prospects of quantum-enhanced AI
Two powerful technologies, quantum computing and artificial intelligence (AI), can potentially disrupt sectors and solve some of society’s greatest problems in practically every industry. A study on how quantum computing and AI can work together. We seek to provide a comprehensive literature review encompassing key contributions and problems from both domains. The AI mission’s unique quantum computing strategy and how quantum algorithms can be used to slave for machine learning models and high-speed, sophisticated, accepted handling are explained. Quantum Computing has been shown to improve AI performance, efficiency, turnaround time, and problem-solving capacities. The research continues by considering what this means for quantum computing in AI, where transformational potential may exist, and where AI work currently limits quantum computing’s use. We also explore future research paths, emphasizing the need for interdisciplinary collaboration to properly utilize those technologies and overcome obstacles. This study should be a foundation for future research and discovery at the interface of quantum computing and AI, leading to ground-breaking applications and results.
Artificial Intelligence (AI) has been evolving at a very rapid pace, with increase in compute power, thus reshaping how complex problems are handled and interpreted from vast amounts of information. AI and newly emerging Quantum Computing have each made significant strides in recent years. Quantum computing has surfaced as a promising new paradigm that leverages on unique quantum phenomena such as like superposition and entanglement to potentially deliver dramatic gains in computational power. Increasingly, researchers are combining these distinct fields into "quantum AI," exploring ways to harness quantum devices for data-intensive tasks, advanced optimisation, and even tentative investigations into emergent machine "awareness." Already researchers have established the quantum advantage over classical AI. This paper outlines the fundamental aspects of AI with respect to quantum computing. It also surveys possible benefits for deep learning and optimization, and considers key concerns related to hardware readiness, algorithmic design, and ethical frameworks. By mapping current progress and pinpointing ongoing hurdles, we provide an entry-level guide to quantum AI's prospects and the steps required for its real-world deployment.