The rise of quantum networks has revolutionized domains such as communication, sensing, and cybersecurity. Despite this progress, current quantum network systems remain limited in scale, are highly application-specific (e.g., for quantum key distribution), and lack a clear road map for global expansion. These limitations are largely driven by a shortage of skilled professionals, limited accessibility to quantum infrastructure, and the high complexity and cost associated with building and operating quantum hardware. To address these challenges, this paper proposes an open-access software-based quantum network virtualization platform designed to facilitate scalable and remote interaction with quantum hardware. The system is built around a cloud application that virtualizes the core hardware components of a lab-scale quantum network testbed, including the time tagger and optical switch, enabling users to perform coincidence counts of the photon entanglements while ensuring fair resource allocation. The fairness is ensured by employing the Hungarian Algorithm to allocate nearly equal effective entanglement rates among users. We provide implementation details and performance analysis from the perspectives of hardware, software, and cloud platform, which demonstrates the functionality and efficiency of the developed prototype.
The rise of quantum networks has revolutionized domains such as communication, sensing, and cybersecurity. Despite this progress, current quantum network systems remain limited in scale, are highly application-specific (e.g., for quantum key distribution), and lack a clear road map for global expansion. These limitations are largely driven by a shortage of skilled professionals, limited accessibility to quantum infrastructure, and the high complexity and cost associated with building and operating quantum hardware. To address these challenges, this paper proposes an open-access software-based quantum network virtualization platform designed to facilitate scalable and remote interaction with quantum hardware. The system is built around a cloud application that virtualizes the core hardware components of a lab-scale quantum network testbed, including the time tagger and optical switch, enabling users to perform coincidence counts of the photon entanglements while ensuring fair resource allocation. The fairness is ensured by employing the Hungarian Algorithm to allocate nearly equal effective entanglement rates among users. We provide implementation details and performance analysis from the perspectives of hardware, software, and cloud platform, which demonstrates the functionality and efficiency of the developed prototype.
Quantum network simulation is an essential step towards developing applications for quantum networks and determining minimal requirements for the network hardware. As it is with classical networking, a simulation ecosystem allows for application development, standardization, and overall community building. Currently, there is limited traction towards building a quantum networking community—there are limited open-source platforms, challenging frameworks with steep learning curves, and strong requirements of software engineering skills. Our Quantum Network Development Kit (QNDK) project aims to solve these issues. It includes a graphical user interface to easily develop and run quantum network simulations with very little code. It integrates various quantum network simulation engines and provides a single interface to them, allowing users to use the features from any of them. Further, it deploys simulation execution in a cloud environment, offloading strong computing requirements to a high-performance computing system. In this paper, we detail the core features of the QNDK and outline the development roadmap to enabling virtual quantum testbeds.
The realization of a Quantum Internet is expected to transform communication networks by enabling intrinsically secure information exchange and distributed quantum applications. However, the experimental study of large-scale quantum networks remains limited by the high cost and operational constraints of quantum hardware. To address this challenge, this paper presents the design of a platform conceived to support research and experimentation in the field of quantum networking. The proposed system provides an emulation environment that mirrors the behavior of a real quantum network, enabling researchers to test protocols and mechanisms through a set of quantum operation instructions accessible via an Application Programming Interface (API). Emulated quantum nodes can be deployed across physically distributed locations, using physical or virtual machines or virtualization containers, allowing experiments to reproduce realistic network conditions and inter-node interactions. This approach enables applications to be executed over a persistent, configurable, and interactive environment that integrates both quantum and classical communication layers. To validate the feasibility of the proposed design, a prototype has been built and used to demonstrate its operation through a multi-hop quantum teleportation proof of concept. The results confirm the viability and flexibility of the architecture, laying the foundation for a distributed framework that facilitates the development and evaluation of quantum network architectures, control mechanisms, and experimental use cases.
As quantum computing continues to progress, there is an increasing need for standardized quantum network technology [1], [2] to connect quantum computers together. Experimental testbeds are necessary in order to develop quantum network software and hardware. Building a scalable testbed for data center-level quantum networks requires overcoming numerous engineering and technical challenges in quantum communications, quantum processing and quantum storage. In this work, we describe a software framework for use with an all-photonic quantum networking testbed. This framework is designed for rapid development and capable of automated execution of quantum experiments. We introduce PnPQ, an open-source, unified driver library for hardware commonly used in quantum optical experiments. On top of this low-level hardware control library, we present software modules for controlling and coordinating quantum network nodes using remote procedure calls (RPC). Finally, we describe high-level software for performing and analyzing quantum photonic experiments using these software modules. As a whole, these software components comprise the first version of a framework that will enable us to implement and test quantum network architectures. This framework is intended to help us move forward from building simple experiments to complex Quantum systems.
The emerging availability of quantum compute resources fosters the examination of its exploitation possibilities in different scientific domains, like artificial intelligence, manufacturing or finance. A significant number of research scientists primarily rely on cloud computing infrastructures while conducting research, whereas access to real (mostly remote) quantum hardware resources requires the deployment and proper configuration of different software components. In this paper we present a hybrid, cloud-based reference architecture that lowers the entry barrier to start new experiments with a wide range of quantum compute resources. The solution facilitates the execution of distributed quantum computing simulations in traditional cloud environments, and also access to several remote quantum compute resources. The reference architecture is highly portable to various cloud platforms, resulting in efficient adaptation and application possibilities by research communities. For demonstration purposes, the paper describes our related experiences on the federated, OpenStack-based research infrastructure of the Hungarian Research Network (abbreviated as HUN-REN).
Cloud computing can be revolutionized by quantum computing which will offer the world more computational power than has ever been seen to solve complex issues.Quantum computing coupled with cloud computing enables the remote access to quantum resources, thus greatly minimizing the cost, technical, and operational difficulties of having quantum hardware owned and maintained in the field.The integration makes large-scale data processing, cryptography, and optimization tasks as well as new applications in artificial intelligence efficient in terms of their computation.This work is a review of the existing approaches, system, and systems to quantum cloud computing, the main algorithms, software applications, implementation plans, and real-life examples.We find that quantum cloud computing provides significant enhancements in computational speed and parallelism, scalability, as well as provides the capability to process data securely and to execute quantum circuits remotely.However, there are still a few obstacles such as stability of qubits, error correction, noise reduction, and effective resource utilization, which restrict the practical use of quantum cloud services.The findings indicate that, irrespective of these challenges, quantum computing with the use of cloud computing platforms offers meaningful potentials to scientific discovery, business, and an AI-based innovation.The paper wraps up by noting that further research should be done to enhance the reliability of quantum hardware, optimize quantum algorithms, and design quantum cloud computing security systems, enabling quantum cloud computing to be adopted more broadly as a more transformative model of computation and ensuring that quantum cloud computing can grow sustainably.
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.