Abstract In the last few years, generative AI has become a revolutionary technology impacting every corner of science and industry, including quantum computing. As quantum computers continue to mature, they will integrate with supercomputers to solve some of the hardest problems in the world. Quantum computers are extremely complex, and both their design and operation face numerous physics and engineering challenges. Fortunately, the combination of AI and supercomputing hardware is well suited to address many of these challenges and is already beginning to accelerate progress across the quantum ecosystem. This chapter will survey some key AI for quantum applications being explored and discuss their implications for quantum strategy.
Abstract In the last few years, generative AI has become a revolutionary technology impacting every corner of science and industry, including quantum computing. As quantum computers continue to mature, they will integrate with supercomputers to solve some of the hardest problems in the world. Quantum computers are extremely complex, and both their design and operation face numerous physics and engineering challenges. Fortunately, the combination of AI and supercomputing hardware is well suited to address many of these challenges and is already beginning to accelerate progress across the quantum ecosystem. This chapter will survey some key AI for quantum applications being explored and discuss their implications for quantum strategy.
Quantum computing introduces revolutionary techniques for information processing by harnessing uniquely quantum phenomena. The emergence of quantum processors, algorithms, and software tools has brought this disruptive technology closer to practical applications. One of the most promising domains where quantum techniques can provide advantages is augmenting artificial intelligence. This chapter provides a comprehensive analysis of the fundamentals underpinning quantum computation, surveys the current hardware landscape, summarizes key quantum algorithms, and highlights promising applications in optimization, sampling, simulation, and machine learning. Engineering challenges in scaling up quantum systems are discussed along with progress in quantum error correction. Projections forecast steady advances towards quantum-enhanced AI based on a timeline of milestones. Realizing these synergies through collaborative interdisciplinary efforts promises to transform computing and catalyse innovations across industries to benefit science and society.
Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI's data-driven learning capabilities, and in fact, many of QC's biggest scaling challenges may ultimately rest on developments in AI. However, bringing leading techniques from AI to QC requires drawing on disparate expertise from arguably two of the most advanced and esoteric areas of computer science. Here we aim to encourage this cross-pollination by reviewing how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC - from device design to applications. We then close by examining its future opportunities and obstacles in this space.
In the ever-changing technological environment, the combination of artificial intelligence (AI) with quantum computing represents an enormous potential frontier. This collaboration has the potential to transform industry, scientific research, and problem-solving approaches. At its heart, AI's function in the quantum computing age includes several transformational components. AI has infiltrated many facets of our everyday lives, from smartphone technology to autonomous car features and novel shopping experiences. Its seamless integration has recently become a major fixture, especially with the emergence of generative AI. Surprisingly, generative AI is set to take quantum computing beyond mere dreams and into the realm of quantum supremacy, where quantum computers vastly outperform traditional approaches. Combining AI and quantum computing might expedite and refine the latter's already impressive skills in addressing complex issues. Such a collaboration might elevate AI from outstanding to game changing.
Quantum computing and artificial intelligence (AI) individually create massive waves in the oceans of technological innovation; however, when these two revolutionary powerhouses are combined, they have the potential to transform industries on a global scale. Quantum computing and AI are merging to redefine high-fidelity decision-making, combining raw processing power (“ultra-compute”) with adaptive intelligence (“ultra-learn”). This synergy unlocks unprecedented capabilities for solving complex problems beyond classical computing's reach. This capability to leverage quantum algorithms enables AI systems to handle easily larger and more sophisticated datasets, executing calculations at unprecedented speeds in the history of science. This leads to new configurations for machine learning, optimization, big data analysis, and more. Integrating these two modern fields must provide a means to circumvent many classical computing constraints, addressing the most challenging issues in medicine, finance, and cybersecurity. In particular, these foundational concepts are vital for any discussions regarding the convergence of quantum computing and AI or similar applications. As we embark on a new technological era, overcoming known computational barriers through the combination of quantum computing and AI models will be essential to achieving those technological milestones that will define our future. After all, innovation driven by AI applied to technology ensures progress.
The rapid convergence of quantum computing and artificial intelligence (AI) marks a transformative phase in computational sciences, promising groundbreaking solutions to problems beyond the reach of classical systems. This paper provides a comprehensive survey of the theoretical foundations, key advancements, and future challenges of integrating quantum computing into AI. The discussion begins with foundational quantum concepts, including the principles of superposition, entanglement, and interference, as well as mathematical frameworks such as Dirac notation and tensor product spaces. These concepts form the backbone of quantum algorithms and architectures designed to enhance AI capabilities. The paper delves into the state-of-the-art in quantum hardware, exploring the construction of quantum gates, circuits, and the innovations driving superconducting qubits, trapped ions, and photonic quantum systems. It reviews the contributions of leading industry and academic players in overcoming scalability and error correction challenges. Building on these hardware advancements, the survey examines prominent quantum algorithms, such as Shor’s algorithm for cryptography, Grover’s algorithm for search and optimization, and their potential to accelerate machine learning tasks. A significant portion of the survey focuses on quantum machine learning (QML), covering core architectures like Quantum Neural Networks (QNNs), Variational Quantum Circuits (VQCs), and Quantum Boltzmann Machines (QBMs). The paper also explores Quantum Support Vector Machines (QSVM) and Quantum Principal Component Analysis (QPCA), emphasizing their implementation, data encoding strategies, and optimization processes. Despite these advances, the paper identifies persistent challenges that hinder widespread adoption, including quantum decoherence, hardware scalability, and the need for modular architectures. Additionally, it considers the ethical and economic implications of quantum-AI integration, particularly as quantum technologies evolve from theoretical models to practical applications. The insights presented here aim to guide researchers, practitioners, and policymakers in shaping the future of this transformative field.
In this chapter, we will be discussing how quantum computing generative artificial intelligence (AI) interacts, its possible advantages and challenges, and various fields of application. By processing complex data and solving optimization problems, quantum computing has a significant edge, while generative AI aims to come up with unique, realistic content in different forms. These two technologies’ merging could result in better machine learning algorithms, enhanced cybersecurity, and fresh developments in drug design and material science. Nevertheless, there are several hurdles to overcome, such as the need for quantum systems to be corrected within errors, how to develop generative models that have been enhanced by quantum technology, and finally, how these technologies might be integrated safely, given consideration to ethical matters.