MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence

• URL: http://arxiv.org/abs/2107.06708v1
• Date: Wed, 14 Jul 2021 13:56:15 GMT
• Title: MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence
• Authors: Armin Moin, Moharram Challenger, Atta Badii and Stephan G\"unnemann
• Abstract summary: In the decade ahead, an unprecedented paradigm shift from classical computing towards Quantum Computing (QC) is expected. We expect the Model-Driven Engineering (MDE) paradigm to be an enabler and a facilitator, when it comes to the quantum and the quantum-classical hybrid applications. This includes not only automated code generation, but also automated model checking and verification, as well as model analysis in the early design phases.
• Score: 1.7969777786551429
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Over the past decade, Artificial Intelligence (AI) has provided enormous new possibilities and opportunities, but also new demands and requirements for software systems. In particular, Machine Learning (ML) has proven useful in almost every vertical application domain. Although other sub-disciplines of AI, such as intelligent agents and Multi-Agent Systems (MAS) did not become promoted to the same extent, they still possess the potential to be integrated into the mainstream technology stacks and ecosystems, for example, due to the ongoing prevalence of the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS). However, in the decade ahead, an unprecedented paradigm shift from classical computing towards Quantum Computing (QC) is expected, with perhaps a quantum-classical hybrid model. We expect the Model-Driven Engineering (MDE) paradigm to be an enabler and a facilitator, when it comes to the quantum and the quantum-classical hybrid applications as it has already proven beneficial in the highly complex domains of IoT, smart CPS and AI with inherently heterogeneous hardware and software platforms, and APIs. This includes not only automated code generation, but also automated model checking and verification, as well as model analysis in the early design phases, and model-to-model transformations both at the design-time and at the runtime. In this paper, the vision is focused on MDE for Quantum AI, and a holistic approach integrating all of the above.

Related papers

• Next-Gen Software Engineering: AI-Assisted Big Models [0.0]
  This paper aims to facilitate a synthesis between models and AI in software engineering. The paper provides an overview of the current status of AI-assisted software engineering. A vision of AI-assisted Big Models in SE is put forth, with the aim of capitalising on the advantages inherent to both approaches.
  arXiv  Detail & Related papers  (2024-09-26T16:49:57Z)
• 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)
• Integration of Quantum Accelerators with High Performance Computing -- A Review of Quantum Programming Tools [0.8477185635891722]
  This study aims to characterize existing quantum programming tools (QPTs) from an HPC perspective. It investigates if existing QPTs have the potential to be efficiently integrated with classical computing models. This work structures a set of criteria into an analysis blueprint that enables HPC scientists to assess whether a QPT is suitable for the quantum-accelerated classical application.
  arXiv  Detail & Related papers  (2023-09-12T12:24:12Z)
• The QUATRO Application Suite: Quantum Computing for Models of Human Cognition [49.038807589598285]
  We unlock a new class of applications ripe for quantum computing research -- computational cognitive modeling. We release QUATRO, a collection of quantum computing applications from cognitive models.
  arXiv  Detail & Related papers  (2023-09-01T17:34:53Z)
• TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework [59.07246314484875]
  TeD-Q is an open-source software framework for quantum machine learning. It seamlessly integrates classical machine learning libraries with quantum simulators. It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
  arXiv  Detail & Related papers  (2023-01-13T09:35:05Z)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• A Model-Driven Engineering Approach to Machine Learning and Software Modeling [0.5156484100374059]
  Models are used in both the Software Engineering (SE) and the Artificial Intelligence (AI) communities. The main focus is on the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS) use cases, where both ML and model-driven SE play a key role.
  arXiv  Detail & Related papers  (2021-07-06T15:50:50Z)
• A backend-agnostic, quantum-classical framework for simulations of chemistry in C++ [62.997667081978825]
  We present the XACC system-level quantum computing framework as a platform for prototyping, developing, and deploying quantum-classical software. A series of examples demonstrating some of the state-of-the-art chemistry algorithms currently implemented in XACC are presented.
  arXiv  Detail & Related papers  (2021-05-04T16:53:51Z)
• Electronic structure with direct diagonalization on a D-Wave quantum annealer [62.997667081978825]
  This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
  arXiv  Detail & Related papers  (2020-09-02T22:46:47Z)

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.