Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine
Learning Use Case
- URL: http://arxiv.org/abs/2402.15542v1
- Date: Fri, 23 Feb 2024 10:36:22 GMT
- Title: Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine
Learning Use Case
- Authors: Sabrina Herbst, Vincenzo De Maio, Ivona Brandic
- Abstract summary: We investigate the use of Edge computing for the integration of quantum machine learning into a distributed computing continuum.
We present preliminary results for quantum machine learning analytics on an IoT scenario.
- Score: 0.6554326244334868
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the advent of the Post-Moore era, the scientific community is faced with
the challenge of addressing the demands of current data-intensive machine
learning applications, which are the cornerstone of urgent analytics in
distributed computing. Quantum machine learning could be a solution for the
increasing demand of urgent analytics, providing potential theoretical speedups
and increased space efficiency. However, challenges such as (1) the encoding of
data from the classical to the quantum domain, (2) hyperparameter tuning, and
(3) the integration of quantum hardware into a distributed computing continuum
limit the adoption of quantum machine learning for urgent analytics. In this
work, we investigate the use of Edge computing for the integration of quantum
machine learning into a distributed computing continuum, identifying the main
challenges and possible solutions. Furthermore, exploring the data encoding and
hyperparameter tuning challenges, we present preliminary results for quantum
machine learning analytics on an IoT scenario.
Related papers
- Hardware-efficient variational quantum algorithm in trapped-ion quantum computer [0.0]
We study a hardware-efficient variational quantum algorithm ansatz tailored for the trapped-ion quantum simulator, HEA-TI.
We leverage programmable single-qubit rotations and global spin-spin interactions among all ions, reducing the dependence on resource-intensive two-qubit gates in conventional gate-based methods.
arXiv Detail & Related papers (2024-07-03T14:02:20Z) - Neural auto-designer for enhanced quantum kernels [59.616404192966016]
We present a data-driven approach that automates the design of problem-specific quantum feature maps.
Our work highlights the substantial role of deep learning in advancing quantum machine learning.
arXiv Detail & Related papers (2024-01-20T03:11:59Z) - Variational data encoding and correlations in quantum-enhanced machine
learning [2.436161840735876]
We develop an effective encoding protocol for translating classical data into quantum states.
We also address the need to counteract the inevitable noise that can hinder quantum acceleration.
By adapting the learning concept from machine learning, we render data encoding a learnable process.
arXiv Detail & Related papers (2023-12-13T07:55:57Z) - Quantum-Assisted Simulation: A Framework for Designing Machine Learning
Models in the Quantum Computing Domain [0.0]
We explore the history of quantum computing, examine existing QML algorithms, and aim to present a simplified procedure for setting up simulations of QML algorithms.
We conducted simulations on a dataset using both machine learning and quantum machine learning approaches.
arXiv Detail & Related papers (2023-11-17T07:33:42Z) - Coreset selection can accelerate quantum machine learning models with
provable generalization [6.733416056422756]
Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning.
We present a unified approach: coreset selection, aimed at expediting the training of QNNs and quantum kernels.
arXiv Detail & Related papers (2023-09-19T08:59:46Z) - 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) - Quantum Federated Learning for Distributed Quantum Networks [9.766446130011706]
We propose a quantum federated learning for distributed quantum networks by utilizing interesting characteristics of quantum mechanics.
A quantum gradient descent algorithm is provided to help clients in the distributed quantum networks to train local models.
A quantum secure multi-party computation protocol is designed, which utilizes the Chinese residual theorem.
arXiv Detail & Related papers (2022-12-25T14:37:23Z) - A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
We focus on the case of learning with a single qubit, using data re-uploading techniques.
We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
arXiv Detail & Related papers (2022-11-23T18:25:32Z) - 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) - Nearest Centroid Classification on a Trapped Ion Quantum Computer [57.5195654107363]
We design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations.
We experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
arXiv Detail & Related papers (2020-12-08T01:10:30Z)
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.