\n Quantum computers are becoming real, and they have the inherent\n potential to significantly impact many application domains. We\n sketch the basics about programming quantum computers, showing that\n quantum programs are typically hybrid consisting of a mixture of\n classical parts and quantum parts. With the advent of quantum\n computers in the cloud, the cloud is a fine environment for\n performing quantum programs. The tool chain available for creating\n and running such programs is sketched. As an exemplary problem we\n discuss efforts to implement quantum programs that are hardware\n independent. A use case from machine learning is outlined. Finally,\n a collaborative platform for solving problems with quantum computers\n that is currently under construction is presented.\n
\n Quantum computers are becoming real, and they have the inherent\n potential to significantly impact many application domains. We\n sketch the basics about programming quantum computers, showing that\n quantum programs are typically hybrid consisting of a mixture of\n classical parts and quantum parts. With the advent of quantum\n computers in the cloud, the cloud is a fine environment for\n performing quantum programs. The tool chain available for creating\n and running such programs is sketched. As an exemplary problem we\n discuss efforts to implement quantum programs that are hardware\n independent. A use case from machine learning is outlined. Finally,\n a collaborative platform for solving problems with quantum computers\n that is currently under construction is presented.\n
Quantum computers are becoming real, and they have the inherent potential to significantly impact many application domains. We sketch the basics about programming quantum computers, showing that quantum programs are typically hybrid consisting of a mixture of classical parts and quantum parts. With the advent of quantum computers in the cloud, the cloud is a fine environment for performing quantum programs. The tool chain available for creating and running such programs is sketched. As an exemplary problem we discuss efforts to implement quantum programs that are hardware independent. A use case from machine learning is outlined. Finally, a collaborative platform for solving problems with quantum computers that is currently under construction is presented.
The convergence of artificial intelligence (AI), quantum computing, and cloud infrastructure represents a paradigm shift in how intelligent systems are designed, trained, and deployed. As AI models continue to demand exponential computational resources, classical systems are increasingly reaching their performance limits in domains such as high-dimensional optimization, complex pattern recognition, and probabilistic modeling. Quantum computing, with its foundational principles of superposition, entanglement, and quantum parallelism, offers a theoretical edge in solving problems that are intractable for conventional hardware. However, the practical deployment of quantum algorithms—particularly those relevant to machine learning (ML) has been hindered by the limited scalability and accessibility of quantum hardware. To bridge this gap, cloud-based quantum computing platforms have emerged, providing remote, programmable access to quantum devices and hybrid runtimes. In this context, the present study offers a comprehensive review of cloud-delivered quantum AI systems, examining core architectures, hybrid quantum-classical algorithms, platform capabilities, and real-world use cases. The paper synthesizes recent advances and performance insights from systems such as Amazon Braket, IBM Quantum, and Azure Quantum, while critically analyzing algorithmic trends like variational quantum circuits and quantum kernel methods. It further explores challenges including circuit depth limitations, latency in hybrid models, and standardization barriers across toolchains. By consolidating current research and implementation patterns, this review aims to inform the design and future trajectory of scalable, cloud-native quantum AI applications.
Quantum Computing (QC) has evolved from a few custom quantum computers, which were only accessible to their creators, to an array of commercial quantum computers that can be accessed on the cloud by anyone. Accessing these cloud quantum computers requires a complex chain of tools that facilitate connecting, programming, simulating algorithms, estimating resources, submitting quantum computing jobs, retrieving results, and more. Some steps in the chain are hardware dependent and subject to change as both hardware and software tools, such as available gate sets and optimizing compilers, evolve. Understanding the trade-offs inherent in this process is essential for evaluating the power and utility of quantum computers. ARLIS has been systematically investigating these environments to understand these complexities. The work presented here is a detailed summary of three months of using such quantum programming environments. We show metadata obtained from these environments, including the connection metrics to the different services, the execution of algorithms, the testing of the effects of varying the number of qubits, comparisons to simulations, execution times, and cost. Our objective is to provide concrete data and insights for those who are exploring the potential of quantum computing. It is not our objective to present any new algorithms or optimize performance on any particular machine or cloud platform; rather, this work is focused on providing a consistent view of a single algorithm executed using out-of-the-box settings and tools across machines, cloud platforms, and time. We present insights only available from these carefully curated data.
The rapid advancement of quantum computing technology presents an opportunity to revolutionize cloud computing platforms, enabling the execution of complex workloads that are beyond the reach of classical systems. This paper explores the potential of quantum-enhanced cloud platforms, focusing on bridging classical and quantum computing to support future workloads. We examine the integration of quantum processors with classical cloud infrastructure, highlighting the challenges and benefits of hybrid architectures that combine the strengths of both paradigms. Key topics include quantum resource management, quantum programming models, and the development of algorithms that leverage quantum speedup for optimization, machine learning, and data analysis. Additionally, we address the scalability, security, and interoperability concerns that must be overcome for effective deployment in real-world cloud environments. By offering insights into the convergence of classical and quantum computing, this paper provides a roadmap for the evolution of cloud platforms capable of supporting next-generation applications and workloads.
Emerging technologies like quantum computing and Artificial Intelligence (AI) are radically changing how we approach computing technology. The fusion of these disciplines within cloud systems offers incredible computing power, which boosts effectiveness and flexibility. Productivity in almost every area – be it optimization, data processing, or machine learning – undergoes a significant transformation. This chapter offers an in-depth study of quantum AI in the scope of the cloud with respect to its basics, sophisticated integration frameworks, and advanced-level practical applications. It also outlines the limits of this new domain, such as hardware and software development, and other secondary issues like safety defects. In addition, this chapter addresses the most notable cloud-based platforms for quantum computing and their game-changing impact on the acceleration of AI development and implementation that will define the future of intelligent computing.
Quantum computing (QC) has experienced rapid growth in recent years with the advent of robust programming environments, readily accessible software simulators and cloud-based QC hardware platforms, and growing interest in learning how to design useful methods that leverage this emerging technology for practical applications. From the perspective of the field of visualization, this article examines research challenges and opportunities along the path from initial feasibility to practical use of QC platforms applied to meaningful problems.