Quantum Computing in the Cloud

By: Aiyaan Hasan, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, rayhasan114@gmail.com


This abstract presents a brief summary of Quantum Computing in the Cloud, stressing the possibilities for collaboration between quantum processors and cloud platforms. The article investigates how the cloud enables remote access to quantum computing resources, allowing a larger audience to experiment with quantum algorithms, simulations, and applications. As quantum technologies progress, the abstract lays the groundwork for comprehending the influence of Quantum Computing in the Cloud on research, innovation, and problem-solving in a variety of disciplines.


The combination of quantum computing and cloud technology has resulted in a paradigm shift known as Quantum Computing in the Cloud.[1] The combination of these factors enables academics, developers, and enterprises to remotely tap into the potential of quantum processors, expanding access to this cutting-edge technology.[2] The possibilities, challenges, and real-world applications that emerge when quantum capabilities meet the scalability and accessibility of cloud platforms are examined in this article.[3]

Figure 1: Quantum Frontier in the Cloud

Quantum Cloud Computing:

Quantum cloud services provided by major cloud platforms such as IBM, Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP) are at the heart of Quantum Computing on the Cloud.[4] These services provide consumers with internet access to quantum processors, reducing the requirement for on-premises quantum hardware. Users can experiment with quantum algorithms, run simulations, and investigate the potential of quantum computing without requiring a large investment in infrastructure.

Quantum-Classical Hybrid Computing:

The integration of quantum processing with traditional computer resources is one distinguishing characteristic enabled by Quantum computer in the Cloud. Users can create algorithms that use both quantum and conventional components with Hybrid Quantum-conventional Computing. This collaboration intends to solve difficult issues more effectively, with quantum processors tackling tasks that are best suited to their strengths while seamlessly integrating with classical algorithms operating on cloud infrastructure.

Simulation of Quantum Circuits:

Simulators are frequently included in cloud-based quantum services, allowing users to experiment with quantum circuits and algorithms before deploying them on genuine quantum hardware. Cloud-based quantum circuit simulation is an important testing ground for developers, allowing them to modify and debug quantum algorithms in a controlled environment. The capacity to simulate quantum algorithms speeds up the development and optimization of quantum algorithms.

Quantum hardware that is easily accessible:

Cloud-based quantum computing potentially opens the door to quantum hardware. Accessible Quantum Hardware enables academics and businesses to investigate the possibilities of quantum computers without facing the costs of creating and maintaining on-premises quantum hardware. This accessibility supports a larger community of quantum developers, encouraging creativity and collaborative exploration of the potential of quantum computing.

Resources for Quantum Learning:

Quantum Learning Resources, such as instructional resources, tutorials, and documentation, are available in combination with cloud-based quantum services. These resources are critical in teaching users about quantum computing principles and programming. Quantum Learning Resources foster a better understanding of quantum algorithms and their applications, which contributes to the development of a competent community of quantum developers.

Quantum Security Solutions:

With the development of quantum computers, quantum security services are becoming more and more significant. Cloud firms are looking on quantum-safe cryptography services and techniques to address the potential security risks associated with quantum computing. Sensitive data protection in the era of quantum computing requires the use of quantum-resistant encryption.


Lastly, a revolutionary way to democratize access to quantum processors is through Quantum Computing on the Cloud. The fusion of cloud platform scalability and quantum capabilities creates new opportunities for creativity and problem solving. The development of quantum computing on the cloud is expected to have a significant impact on a variety of industries, from cybersecurity to research and development, changing the landscape of technology and paving the way for a time when everyone will be able to utilize quantum computing’s capabilities..


  1. Ravi, G. S., Smith, K. N., Gokhale, P., & Chong, F. T. (2021, November). Quantum Computing in the Cloud: Analyzing job and machine characteristics. In 2021 IEEE International Symposium on Workload Characterization (IISWC) (pp. 39-50). IEEE.
  2. Barzen, J., Leymann, F., Falkenthal, M., Vietz, D., Weder, B., & Wild, K. (2020, May). Relevance of near-term quantum computing in the cloud: A humanities perspective. In International Conference on Cloud Computing and Services Science (pp. 25-58). Cham: Springer International Publishing.
  3. Kaiiali, M., Sezer, S., & Khalid, A. (2019, June). Cloud computing in the quantum era. In 2019 IEEE Conference on Communications and Network Security (CNS) (pp. 1-4). IEEE.
  4. Singh, H., & Sachdev, A. (2014, February). The quantum way of cloud computing. In 2014 International Conference on Reliability Optimization and Information Technology (ICROIT) (pp. 397-400). Ieee.
  5. Xu, Z., He, D., Vijayakumar, P., Gupta, B., & Shen, J. (2021). Certificateless public auditing scheme with data privacy and dynamics in group user model of cloud-assisted medical WSNs. IEEE Journal of Biomedical and Health Informatics.
  6. Liu, R. W., Guo, Y., Lu, Y., Chui, K. T., & Gupta, B. B. (2022). Deep network-enabled haze visibility enhancement for visual IoT-driven intelligent transportation systems. IEEE Transactions on Industrial Informatics19(2), 1581-1591.
  7. Zhou, Z., Li, Y., Li, J., Yu, K., Kou, G., Wang, M., & Gupta, B. B. (2022). Gan-siamese network for cross-domain vehicle re-identification in intelligent transport systems. IEEE Transactions on Network Science and Engineering.
  8. Zhang, Q., Guo, Z., Zhu, Y., Vijayakumar, P., Castiglione, A., & Gupta, B. B. (2023). A deep learning-based fast fake news detection model for cyber-physical social services. Pattern Recognition Letters168, 31-38.
  9. Deveci, M., Gokasar, I., Pamucar, D., Zaidan, A. A., Wen, X., & Gupta, B. B. (2023). Evaluation of Cooperative Intelligent Transportation System scenarios for resilience in transportation using type-2 neutrosophic fuzzy VIKOR. Transportation Research Part A: Policy and Practice172, 103666.

Cite As

Hasan A. (2023) Quantum Computing in the Cloud, Insights2Techinfo, pp.1

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