Towards Federated Learning on the Quantum Internet
- URL: http://arxiv.org/abs/2402.09902v1
- Date: Thu, 15 Feb 2024 11:58:42 GMT
- Title: Towards Federated Learning on the Quantum Internet
- Authors: Leo S\"unkel and Michael K\"olle and Tobias Rohe and Thomas Gabor
- Abstract summary: The quantum internet may allow a plethora of applications such as distributed or blind quantum computing.
We evaluate a potential application for the quantum internet, namely quantum federated learning.
- Score: 2.869565958847859
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While the majority of focus in quantum computing has so far been on
monolithic quantum systems, quantum communication networks and the quantum
internet in particular are increasingly receiving attention from researchers
and industry alike. The quantum internet may allow a plethora of applications
such as distributed or blind quantum computing, though research still is at an
early stage, both for its physical implementation as well as algorithms; thus
suitable applications are an open research question. We evaluate a potential
application for the quantum internet, namely quantum federated learning. We run
experiments under different settings in various scenarios (e.g. network
constraints) using several datasets from different domains and show that (1)
quantum federated learning is a valid alternative for regular training and (2)
network topology and nature of training are crucial considerations as they may
drastically influence the models performance. The results indicate that more
comprehensive research is required to optimally deploy quantum federated
learning on a potential quantum internet.
Related papers
- Quantum Algorithms and Applications for Open Quantum Systems [1.7717834336854132]
We provide a succinct summary of the fundamental theory of open quantum systems.
We then delve into a discussion on recent quantum algorithms.
We conclude with a discussion of pertinent applications, demonstrating the applicability of this field to realistic chemical, biological, and material systems.
arXiv Detail & Related papers (2024-06-07T19:02:22Z) - 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) - Practical limitations on robustness and scalability of quantum Internet [0.7499722271664144]
We study the limitations on the scaling and robustness of quantum Internet.
We present practical bottlenecks for secure communication, delegated computing, and resource distribution among end nodes.
For some examples of quantum networks, we present algorithms to perform different quantum network tasks of interest.
arXiv Detail & Related papers (2023-08-24T12:32:48Z) - Entanglement-Assisted Quantum Networks: Mechanics, Enabling
Technologies, Challenges, and Research Directions [66.27337498864556]
This paper presents a comprehensive survey of entanglement-assisted quantum networks.
It provides a detailed overview of the network structure, working principles, and development stages.
It also emphasizes open research directions, including architecture design, entanglement-based network issues, and standardization.
arXiv Detail & Related papers (2023-07-24T02:48:22Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - 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) - 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) - 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) - QuantumFed: A Federated Learning Framework for Collaborative Quantum
Training [10.635097939284751]
We propose QuantumFed, a quantum federated learning framework to have multiple quantum nodes with local quantum data train a mode together.
Our experiments show the feasibility and robustness of our framework.
arXiv Detail & Related papers (2021-06-16T20:28:11Z) - 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)
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.