Quantum Serverless Paradigm and Application Development using the QFaaS Framework

• URL: http://arxiv.org/abs/2407.02828v1
• Date: Wed, 3 Jul 2024 06:12:55 GMT
• Title: Quantum Serverless Paradigm and Application Development using the QFaaS Framework
• Authors: Hoa T. Nguyen, Bui Binh An Pham, Muhammad Usman, Rajkumar Buyya,
• Abstract summary: This chapter introduces the concept of serverless quantum computing with examples using QF. The framework utilizes the serverless computing model to simplify quantum application development and deployment. The chapter provides comprehensive documentation and guidelines for deploying and using QF.
• Score: 17.398771276317575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computing has the potential to solve complex problems beyond the capabilities of classical computers. However, its practical use is currently limited due to early-stage quantum software engineering and the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. To address this issue, this chapter introduces the concept of serverless quantum computing with examples using QFaaS, a practical Quantum Function-as-a-Service framework. This framework utilizes the serverless computing model to simplify quantum application development and deployment by abstracting the complexities of quantum hardware and enhancing application portability across different quantum software development kits and quantum backends. The chapter provides comprehensive documentation and guidelines for deploying and using QFaaS, detailing the setup, component deployment, and examples of service-oriented quantum applications. This framework offers a promising approach to overcoming current limitations and advancing the practical software engineering of quantum computing.

Related papers

• Advancing Quantum Software Engineering: A Vision of Hybrid Full-Stack Iterative Model [5.9478154558776435]
  This paper introduces a vision for Quantum Software Develop- ment lifecycle. It proposes a hybrid full-stack iterative model that integrates quantum and classical computing.
  arXiv  Detail & Related papers  (2024-03-18T11:18:33Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• QPanda: high-performance quantum computing framework for multiple application scenarios [15.954489124674394]
  This paper proposes QPanda, an application scenario-oriented quantum programming framework with high-performance simulation. It implements high-performance simulation of quantum circuits, a configuration of the fusion processing backend of quantum computers and supercomputers, and compilation and optimization methods of quantum programs for NISQ machines.
  arXiv  Detail & Related papers  (2022-12-29T07:38:50Z)
• Assessing requirements to scale to practical quantum advantage [56.22441723982983]
  We develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required for large-scale quantum applications. We assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage. A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack.
  arXiv  Detail & Related papers  (2022-11-14T18:50:27Z)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• QFaaS: A Serverless Function-as-a-Service Framework for Quantum Computing [22.068803245816266]
  We propose a Quantum Function-as-a-Service framework to advance quantum computing. Our framework provides essential components of a quantum serverless platform to simplify the software development and adapt to the quantum cloud computing paradigm. This paper proposes architectural design, principal components, the life cycle of hybrid quantum-classical function, operation workflow, and implementation of QF.
  arXiv  Detail & Related papers  (2022-05-30T04:18:53Z)
• Full-stack quantum computing systems in the NISQ era: algorithm-driven and hardware-aware compilation techniques [1.3496450124792878]
  We will provide an overview on current full-stack quantum computing systems. We will emphasize the need for tight co-design among adjacent layers as well as vertical cross-layer design.
  arXiv  Detail & Related papers  (2022-04-13T13:26:56Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)
• QSOC: Quantum Service-Oriented Computing [3.2786644738211725]
  This paper introduces Quantum Service-Oriented Computing (QSOC) It includes a model-driven methodology to allow enterprise DevOps teams to compose, configure and operate enterprise applications without intimate knowledge on the underlying quantum infrastructure. It advocates knowledge reuse, separation of concerns, resource optimization, and mixed quantum- & conventional QSOC applications.
  arXiv  Detail & Related papers  (2021-05-04T09:05:10Z)
• Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale [57.84751206630535]
  We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework. The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators. By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
  arXiv  Detail & Related papers  (2021-04-21T13:26:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.