Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing

• URL: http://arxiv.org/abs/2401.10623v3
• Date: Mon, 29 Jul 2024 13:29:26 GMT
• Title: Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing
• Authors: Wolfgang Maass, Ankit Agrawal, Alessandro Ciani, Sven Danz, Alejandro Delgadillo, Philipp Ganser, Pascal Kienast, Marco Kulig, Valentina König, Nil Rodellas-Gràcia, Rivan Rughubar, Stefan Schröder, Marc Stautner, Hannah Stein, Tobias Stollenwerk, Daniel Zeuch, Frank K. Wilhelm,
• Abstract summary: We propose a framework for a quantum computing-enhanced service ecosystem for simulation in manufacturing. We analyse two high-value use cases with the aim of a quantitative evaluation of these new computing paradigms for industrially-relevant settings.
• Score: 56.61654656648898
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computing (QC) and machine learning (ML), taken individually or combined into quantum-assisted ML (QML), are ascending computing paradigms whose calculations come with huge potential for speedup, increase in precision, and resource reductions. Likely improvements for numerical simulations in engineering imply the possibility of a strong economic impact on the manufacturing industry. In this project report, we propose a framework for a quantum computing-enhanced service ecosystem for simulation in manufacturing, consisting of various layers ranging from hardware to algorithms to service and organizational layers. In addition, we give insight into the current state of the art of applications research based on QC and QML, both from a scientific and an industrial point of view. We further analyse two high-value use cases with the aim of a quantitative evaluation of these new computing paradigms for industrially-relevant settings.

Related papers

• Integrating Quantum Computing Resources into Scientific HPC Ecosystems [29.1407119677928]
  Quantum Computing offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. QC faces challenges due to the noisy intermediate-scale quantum era's inherent external noise issues. This paper outlines plans to unlock new computational possibilities, driving forward scientific inquiry and innovation in a wide array of research domains.
  arXiv  Detail & Related papers  (2024-08-28T22:44:54Z)
• A Framework for Integrating Quantum Simulation and High Performance Computing [0.0]
  We describe a framework to help streamline access to quantum simulation software running on HPC resources. This includes an interface for circuit-based quantum computing tasks, as well as the necessary resource management infrastructure.
  arXiv  Detail & Related papers  (2024-08-15T11:48:14Z)
• Benchmarking Quantum Computer Simulation Software Packages: State Vector Simulators [0.0]
  We benchmark several software packages capable of simulating quantum dynamics with a special focus on HPC capabilities. We develop a containerized toolchain for benchmarking a large set of simulation packages on a local HPC cluster using different parallelisation capabilities. Our results can help finding the right package for a given simulation task and lay the foundation for a systematic community effort to benchmark and validate upcoming versions of existing and also newly developed simulation packages.
  arXiv  Detail & Related papers  (2024-01-17T09:34:28Z)
• A Design Framework for the Simulation of Distributed Quantum Computing [2.969582361376132]
  Growing demand for large-scale quantum computers is pushing research on Distributed Quantum Computing (DQC) Recent experimental efforts have demonstrated some of the building blocks for such a design. DQC systems are clusters of quantum processing units connected by means of quantum network infrastructures.
  arXiv  Detail & Related papers  (2023-06-20T13:52:05Z)
• iQuantum: A Case for Modeling and Simulation of Quantum Computing Environments [22.068803245816266]
  iQuantum is a first-of-its-kind simulation toolkit that can model hybrid quantum-classical computing environments. This paper presents the quantum computing system model, architectural design, proof-of-concept implementation, potential use cases, and future development of iQuantum.
  arXiv  Detail & Related papers  (2023-03-28T04:51:32Z)
• Potential and limitations of quantum extreme learning machines [55.41644538483948]
  We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements. Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
  arXiv  Detail & Related papers  (2022-10-03T09:32:28Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)
• QuaSiMo: A Composable Library to Program Hybrid Workflows for Quantum Simulation [48.341084094844746]
  We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation. We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
  arXiv  Detail & Related papers  (2021-05-17T16:17:57Z)
• 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)
• 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.