Distributed Quantum Computing: Applications and Challenges
- URL: http://arxiv.org/abs/2410.00609v1
- Date: Tue, 1 Oct 2024 11:55:04 GMT
- Title: Distributed Quantum Computing: Applications and Challenges
- Authors: Juan C. Boschero, Niels M. P. Neumann, Ward van der Schoot, Thom Sijpesteijn, Robert Wezeman,
- Abstract summary: Distributed quantum computing aims to scale quantum computers through the linking of different individual quantum computers.
This study seeks to give an overview of this technology on an application-level, considering both use cases and implementation considerations.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum computing is presently undergoing rapid development to achieve a significant speedup promised in certain applications. Nonetheless, scaling quantum computers remains a formidable engineering challenge, prompting exploration of alternative methods to achieve the promised quantum advantage. An example is given by the concept of distributed quantum computing, which aims to scale quantum computers through the linking of different individual quantum computers. Additionally, distributed quantum computing opens the way to new applications on the longer term. This study seeks to give an overview of this technology on an application-level, considering both use cases and implementation considerations. In this way, this work aims to push forward the field of distributed quantum computing, aiming for real-world distributed quantum systems in the near future.
Related papers
- The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data.
We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits.
Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z) - Review of Distributed Quantum Computing. From single QPU to High Performance Quantum Computing [2.2989970407820484]
distributed quantum computing aims to boost the computational power of current quantum systems.
From quantum communication protocols to entanglement-based distributed algorithms, each aspect contributes to the mosaic of distributed quantum computing.
Our objective is to provide an exhaustive overview for experienced researchers and field newcomers.
arXiv Detail & Related papers (2024-04-01T17:38:18Z) - Quantum Computing: Vision and Challenges [16.50566018023275]
We discuss cutting-edge developments in quantum computer hardware advancement and subsequent advances in quantum cryptography, quantum software, and high-scalability quantum computers.
Many potential challenges and exciting new trends for quantum technology research and development are highlighted in this paper for a broader debate.
arXiv Detail & Related papers (2024-03-04T17:33:18Z) - Quantum computing: principles and applications [3.717431207294639]
We introduce the basic principles of quantum computing and the multilayer architecture for a quantum computer.
Based on a mature experimental platform, the Nuclear Magnetic Resonance (NMR) platform, we introduce the basic steps to experimentally implement quantum computing.
arXiv Detail & Related papers (2023-10-13T20:12:28Z) - 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) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - Programming with Quantum Mechanics [0.7219077740523683]
Quantum computing is an emerging paradigm that opens a new era for exponential computational speedup.
This tutorial gives a broad view of quantum computing, abstracting most of the mathematical formalism and proposing a hands-on with the quantum programming language Ket.
arXiv Detail & Related papers (2022-10-27T14:38:42Z) - 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) - Resource Allocation in Quantum Networks for Distributed Quantum
Computing [0.0]
Current trend suggests that quantum computing will become available at scale for commercial purposes in the near future.
Quantum Internet requires the interconnection of quantum computers by quantum links and repeaters to exchange entangled quantum bits.
This paper investigates the requirements and objectives of smart computing on distributed nodes from the perspective of quantum network provisioning.
arXiv Detail & Related papers (2022-03-11T10:46:31Z) - Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems.
We propose an algorithm to implement imaginary time propagation on a quantum computer.
arXiv Detail & Related papers (2021-02-24T12:48:00Z) - An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem.
The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
arXiv Detail & Related papers (2020-05-06T14:18:44Z)
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.