Starting from the idea of Quantum Computing which is a concept that dates back to 80s, we come to the present day where we can perform calculations on real quantum computers. This sudden development of technology opens up new scenarios that quickly lead to the desire and the real possibility of integrating this technology into current software architectures. The usage of frameworks that allow computation to be performed directly on quantum hardware poses a series of challenges. This document describes a an architectural framework that addresses the problems of integrating an API exposed Quantum provider in an existing Enterprise architecture and it provides a minimum viable product (MVP) solution that really merges classical quantum computers on a basic scenario with reusable code on GitHub repository. The solution leverages a web-based frontend where user can build and select applications/use cases and simply execute it without any further complication. Every triggered run leverages on multiple backend options, that include a scheduler managing the queuing mechanism to correctly schedule jobs and final results retrieval. The proposed solution uses the up-to-date cloud native technologies (e.g. Cloud Functions, Containers, Microservices) and serves as a general framework to develop multiple applications on the same infrastructure.
Starting from the idea of Quantum Computing which is a concept that dates back to 80s, we come to the present day where we can perform calculations on real quantum computers. This sudden development of technology opens up new scenarios that quickly lead to the desire and the real possibility of integrating this technology into current software architectures. The usage of frameworks that allow computation to be performed directly on quantum hardware poses a series of challenges. This document describes a an architectural framework that addresses the problems of integrating an API exposed Quantum provider in an existing Enterprise architecture and it provides a minimum viable product (MVP) solution that really merges classical quantum computers on a basic scenario with reusable code on GitHub repository. The solution leverages a web-based frontend where user can build and select applications/use cases and simply execute it without any further complication. Every triggered run leverages on multiple backend options, that include a scheduler managing the queuing mechanism to correctly schedule jobs and final results retrieval. The proposed solution uses the up-to-date cloud native technologies (e.g. Cloud Functions, Containers, Microservices) and serves as a general framework to develop multiple applications on the same infrastructure.
Quantum computing is increasingly accessed through cloud-based hybrid environments, but practical adoption depends on more than exposing remote quantum backends. It also requires managed abstractions for orchestrating classical and quantum stages, handling queues, binding backends, and integrating surrounding workflow logic. This paper presents a comprehensive survey of Quantum Serverless from that perspective. Our contribution is twofold: (1) we consolidate the key architectural components of Quantum Serverless into a unified reference architecture; and (2) we evaluate current industry and research offerings through a KPI-driven framework covering ecosystem coverage, service operability, queue handling, pricing flexibility, near-real-time readiness, and production maturity. Using this framework, we analyze 18 quantum cloud platforms and show that the ecosystem is growing but remains unevenly mature. Queue-based execution is already a baseline capability, whereas stronger serverless characteristics, such as Function-as-a-Service, Containers-as-a-Service, near-real-time responsiveness, and production readiness, remain limited. Overall, current Quantum Serverless is best understood as a queue-driven hybrid orchestration model rather than a fully mature cloud-native serverless layer. The survey identifies the main maturity gaps and outlines directions for future development.
The emergence of quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. The ability of quantum computers to scale computations beyond what the current computers are capable of implies better performance and efficiency for certain algorithmic tasks. However, to benefit from such improvement, quantum computers must be integrated with existing software systems, a process that is not straightforward. In this paper, we propose a unified execution model that addresses the challenges that emerge from building hybrid classical-quantum applications at scale. Following the Design Science Research methodology, we proposed a convention for mapping quantum resources and artifacts to Kubernetes concepts. Then, in an experimental Kubernetes cluster, we conducted experiments for scheduling and executing quantum tasks on both quantum simulators and hardware. The experimental results demonstrate that the proposed platform Qubernetes (or Kubernetes for quantum) exposes the quantum computation tasks and hardware capabilities following established cloud-native principles, allowing seamless integration into the larger Kubernetes ecosystem. The quantum computing potential cannot be realized without seamless integration into classical computing. By validating that it is practical to execute quantum tasks in a Kubernetes infrastructure, we pave the way for leveraging the existing Kubernetes ecosystem as an enabler for hybrid classical-quantum computing.
Quantum Computing as a Service (QCaaS) is changing the face of enterprise IT by providing access to quantum processing on cloud interfaces without having to invest in specialized hardware. The article offers a quantum-classical integration framework that considers architectural issues such as hybrid design policies, orchestration policies, workload abstraction and scheduling, and error resilience in probabilistic environments. The challenges mentioned in the implementation, such as API stability problems, limited resources, security, talent staffing, risk mitigation, and financial services, present real-world applications. Applications: Enterprise optimization, Enterprise supply chain and logistics optimization, Enterprise quantum-enhanced machine learning, Enterprise cryptography and security, Enterprise materials science simulation, Enterprise ROI assessment frameworks. Strategic adoption guidelines include criteria in the selection of pilot projects, strategies in vendor partnerships, development of an integration roadmap, approaches to performance benchmarking, and alignment with enterprise IT strategies. The QCaaS integration will present a viable channel through which organizations can tap into the benefits of quantum without disrupting their operations with respect to the current cloud infrastructure.
Quantum computing is rapidly reaching a point in which its application design and engineering aspects have to be seriously considered. However, quantum software engineering is still in its infancy with numerous challenges, especially in dealing with the diversity of quantum programming languages and noisy intermediate-scale quantum systems. To alleviate these challenges, we propose QFaaS, a novel Quantum Function-as-a-Service framework, which leverages the advantages of the serverless model and state-of-the-art techniques to advance practical quantum computing in the NISQ era. Our framework provides essential elements of a serverless quantum system to streamline service-oriented quantum application development in cloud environments, such as combining hybrid quantum-classical computation, automating the backend selection, and adapting quantum DevOps techniques. QFaaS offers the first full-stack and unified quantum serverless platform by integrating multiple well-known quantum software development kits (Qiskit, Q#, Cirq, and Braket), quantum simulators, and cloud providers (IBM Quantum and Amazon Braket). This paper proposes the concept of quantum functions, software life cycle, system design, operation workflows, and implementation of QFaaS. We also present two practical use cases and performance evaluations on today’s quantum computers and simulators to demonstrate our framework’s capability to ease the burden on traditional engineers to expedite the ongoing quantum software transition.
Applied quantum computing research has often focused on determining business-driven use-cases and applications of quantum algorithms which may provide advantage, either runtime or solution quality, over their classical counterparts. Often missed, yet equally important, is the discussion on how to actually run quantum software applications or models in a production environment where they run in an event-based or scheduled manner thereby providing the results to the enterprise which has spent time upskilling their development team and developing the quantum software solution.In this paper, we introduce a hybrid classical-quantum cloud architecture blueprint which starts from the code-level software development level, and extends to the production-ready quantum software application level. We then, for the first time, introduce a novel sample optimization application used in the energy industry for Peer-2-Peer energy trading and deploy it using the proposed cloud architecture blueprint. We demonstrate sample output of the live-running application where trading optimizations occurs on an hourly basis via remote connection to a quantum computing cloud backend.
Despite the promise and potential of quantum computing, most of the current day developments of quantum computing have several limitations for relevant practical use. As a result, hybrid classical-quantum computing has emerged as a viable solution. This paper discusses the software aspects of hybrid classical-quantum computing solutions to understand and examine how hybrid computing solutions can be expected to work in practice. This paper examines existing software solutions (cloud applications, middleware frameworks, and API gateways) that can enable classical and quantum computing to work together. For practitioners the paper provides a curated list of algorithms, full stack libraries, simulators, and cloud service providers that are likely to be central to the development of any hybrid computing solutions. We conclude by identifying an agenda for future research on classical-quantum hybrid software engineering, and some near-term priorities for development of functional, robust hybrid software systems. The paper argues that due to the deep dependency of quantum execution on classical pre- and post-processing, hybrid computing may represent a near-permanent outcome of classical-quantum computing rather than a transitional workaround.