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.
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.
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.
The growing demand of better functionality in the distributed computing systems has discovered the limitations of the conventional cloud systems in operating under the complicated model, particularly with the computation, security risk, and processing time in a low latency. Even though the distributed systems can work with a much larger number of clients, as they have more computing power, in practice they are limited by the speed of traditional processors and classical encryption model protection. The provided paper describes a quantum-enabled model of cloud computing to facilitate the deployment of quantum processors within the framework of the infrastructure of distributed systems and also facilitates hybrid classical-quantum computing on the bundled runtime level. The proposed model employs quantum algorithms to optimize the optimization and speed up the performances of solving problems, improve security by using quantum-proven cryptographic, and distribute resources to the distributed nodes in the cloud computing system in an intelligent manner. Computational structure implies that it has a layered architecture that comprises of quantum processing units, cloud-level software coordination, and quantum communication protocols. Qiskit and cloud-native applications are used to test the model by simulation. Results indicate that the run time on complex optimization problems may be reduced by a maximum of 45 per cent, and the ability to provide more data security against quantum threat. Results provide a clue on the possible transformation quantum can impose on the next generation cloud-based distributed systems and transform continuous work and research to enable sustainable, secure and efficient infrastructures to maximize future computing.
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 increasing accessibility of quantum computing resources encourages research into the potential applications of this technology in a variety of scientific fields, including artificial intelligence, manufacturing, and finance.While a large number of research scientists do their work largely using cloud computing infrastructures, access to real (sometimes distant) quantum hardware resources necessitates the deployment and appropriate setup of several software components.We provide a hybrid cloud-based reference architecture in this study that makes it easier to launch new experiments utilizing a variety of quantum computing resources.The technique makes it easier to access many remote quantum compute resources and to run distributed quantum computing simulations in conventional cloud settings.Because the reference design is so adaptable to many cloud platforms, there are many opportunities for applications.
Quantum computing promises to tackle technological and industrial problems insurmountable for classical computers. However, today's quantum computers still have limited demonstrable functionality, and it is expected that scaling up to millions of qubits is required for them to live up to this touted promise. The feasible route in achieving practical quantum advantage goals is to implement a hybrid operational mode that realizes the cohesion of quantum and classical computers. Here we present a hybrid quantum cloud based on a memory-centric and heterogeneous multiprocessing architecture, integrated into a high-performance computing data center grade environment. We demonstrate that utilizing the quantum cloud, our hybrid quantum algorithms including Quantum Encoding (QuEnc), Hybrid Quantum Neural Networks and Tensor Networks enable advantages in optimization, machine learning, and simulation fields. We show the advantage of hybrid algorithms compared to standard classical algorithms in both the computational speed and quality of the solution. The achieved advance in hybrid quantum hardware and software makes quantum computing useful in practice today.
Quantum computing is a new emerging technology which is capable of performing specific types of calculations more efficiently than classical computers. Yet quantum computers have their own limitations in terms of noise, decoherence, and qubits numbers, so the current research focus on hybrid quantum-classical computation where classical computers work in conjunction with quantum devices to perform computation. This chapter discusses the architecture of quantum-ready clouds and describes how to effectively run a hybrid quantum-classical workflows over cloud environment. It introduces the middleware and Application Programming Interface (API) in order to support communication between cloud environment and quantum hardware. Furthermore, the chapter shows how the orchestration mechanism to coordinate the classical cloud resources and quantum processors, and presents a simple workflow model for data routing, processing, and analysis utilizing classical/cloud and quantum hardware.