Quantum computing has emerged as a revolutionary approach to solving complex problems that demand powerful computational capabilities. With various cloud quantum computing services available, users are presented with different architectures and performances. In this study, we conduct research on select cloud quantum computing services to evaluate their performances and compare them. Using two different methods, visual programming and Qiskit, we test these services and analyze the impact of factors such as the number of qubits per backend and shots per run on execution time. Our findings provide users with insights to decide which cloud quantum computing services offer better performance or faster execution times based on their specific requirements.
Quantum computing has emerged as a revolutionary approach to solving complex problems that demand powerful computational capabilities. With various cloud quantum computing services available, users are presented with different architectures and performances. In this study, we conduct research on select cloud quantum computing services to evaluate their performances and compare them. Using two different methods, visual programming and Qiskit, we test these services and analyze the impact of factors such as the number of qubits per backend and shots per run on execution time. Our findings provide users with insights to decide which cloud quantum computing services offer better performance or faster execution times based on their specific requirements.
A cloud quantum computer is a quantum computer that can be accessed in a cloud environment through a network. Today, there are numbers of cloud quantum computing services that can be accessed by users. They are used to solve complex problems that require powerful computing. Different cloud quantum computing services deliver different architecture and performances. In our study, we conducted a research on some services to test and evaluate the performances of different cloud quantum computing services and make a comparison out of it. The test will be conducted using two different methods such as visual programming and qiskit. From the result, we can see that the amount of qubit per backend and shots per run pretty much affect the execution time of a cloud quantum computing. This test will give the users some insight and enables them to decide which cloud quantum computing services deliver better performance or faster execution time based on the specification each cloud quantum computer offers.
The integration of quantum computing into cloud services represents a revolutionary advancement in processing capabilities.Quantum computers, utilizing principles of quantum mechanics, offer unparalleled computational speeds, allowing cloud providers to deliver quantum processing units alongside traditional CPUs and GPUs.Accessible through Quantum Computing as a Service (QCaaS), this facilitates businesses and researchers in harnessing quantum power without substantial upfront investments.Hybrid cloud solutions seamlessly blend classical and quantum processing, opening new frontiers in optimization and cryptography.The integration accelerates quantum machine learning and supports global quantum research.Concurrently, cloud computing's elasticity and pay-as-you-go model attract businesses.This paper explores the reciprocal impact of quantum technology on cloud computing and vice versa, recognizing the rapid development of quantum technology.The study evaluates cloud quantum computing services, revealing key factors influencing execution time.This research provides insights for informed decision-making based on service specifications.
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.
Abstract: This article explores the convergence of quantum computing and cloud technologies, representing a pivotal advancement in computational power with far-reaching implications across various industries and scientific domains. It examines the current state of quantum computing, elucidating its fundamental principles and recent hardware advancements, while comparing its capabilities to classical computing systems. The integration of quantum computing with cloud platforms is analyzed, highlighting the emergence of cloud-based quantum services and their advantages in democratizing access to this cutting-edge technology. The article delves into potential applications of quantum cloud computing, including cryptography, material science, artificial intelligence, and financial modeling, showcasing its transformative potential. Critical challenges facing quantum cloud computing are addressed, such as error correction, scalability issues, and the development of quantumsafe encryption methods. Looking ahead, the article discusses prospects and research directions, including anticipated breakthroughs in quantum hardware, emerging quantum cloud architectures, and the potential impact on various industries. By synthesizing current research and industry developments, this comprehensive overview provides insights into the revolutionary potential of quantum cloud computing and its role in shaping the future of computational capabilities.
Quantum computing was proposed to simulate processes that surpass the capabilities of its counterpart, classical computing. Utilizing the principles of quantum mechanics, it improves the computing power of quantum computing. Top developers namely IBM, Rigetti, D-Wave, Qutech and Google have invested greatly in the technology. Nowadays, users can access the quantum computing system publicly over the network in a cloud environment, this system architecture is known as cloud-based quantum computing. However, different developers deliver different architecture and functionality of the system on their platforms. This has indirectly spawned a question of which cloud-based quantum computing platform is a better option based on certain specific requirements by an individual or group. The main objective of this study is to provide a proposed framework using the existing cloud-based service of quantum computing based on previous studies for users with their specific demands.
Quantum computing has emerged as a transformative technology capable of solving complex computational problems beyond the capabilities of classical systems. However, severe hardware limitations, qubit decoherence, and high implementation costs prevent it from replacing classical cloud infrastructure. Instead, cloud and quantum computing are evolving as complementary technologies. Through Quantum-as-a-Service (QaaS) and hybrid cloud–quantum architectures, cloud platforms provide scalable infrastructure, data management, and resource orchestration, while quantum processors accelerate specialized computational tasks. This review systematically examines the relationship between cloud computing and quantum computing by analyzing their technical differences, capabilities, application areas, current challenges, limitations, and future research directions. The findings indicate that cloud computing will continue to serve as the backbone of modern digital infrastructure, while quantum computing will enhance its capabilities for solving complex computational problems. Together, these technologies are expected to drive the next generation of intelligent, scalable, and high-performance computing systems. Keywords: cloud computing; quantum computing; Quantum as a Service (QaaS); Hybrid Cloud–Quantum Architecture; Post-Quantum Security; QMLOps