Quantum Algorithms for Unsupervised Machine Learning and Neural Networks
- URL: http://arxiv.org/abs/2111.03598v1
- Date: Fri, 5 Nov 2021 16:36:09 GMT
- Title: Quantum Algorithms for Unsupervised Machine Learning and Neural Networks
- Authors: Jonas Landman
- Abstract summary: We introduce quantum algorithms to solve tasks such as matrix product or distance estimation.
These results are then used to develop new quantum algorithms for unsupervised machine learning.
We will also present new quantum algorithms for neural networks, or deep learning.
- Score: 2.28438857884398
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In this thesis, we investigate whether quantum algorithms can be used in the
field of machine learning for both long and near term quantum computers. We
will first recall the fundamentals of machine learning and quantum computing
and then describe more precisely how to link them through linear algebra: we
introduce quantum algorithms to efficiently solve tasks such as matrix product
or distance estimation. These results are then used to develop new quantum
algorithms for unsupervised machine learning, such as k-means and spectral
clustering. This allows us to define many fundamental procedures, in particular
in vector and graph analysis. We will also present new quantum algorithms for
neural networks, or deep learning. For this, we introduce an algorithm to
perform a quantum convolution product on images, as well as a new way to
perform a fast tomography on quantum states. We prove that these quantum
algorithms are faster versions of equivalent classical algorithms, but exhibit
random effects due to the quantum nature of the computation. Many simulations
have been carried out to study these effects and measure their learning
accuracy on real data. Finally, we will present a quantum orthogonal neural
network circuit adapted to the currently available small and imperfect quantum
computers. This allows us to perform real experiments to test our theory.
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) - Quantum Information Processing with Molecular Nanomagnets: an introduction [49.89725935672549]
We provide an introduction to Quantum Information Processing, focusing on a promising setup for its implementation.
We introduce the basic tools to understand and design quantum algorithms, always referring to their actual realization on a molecular spin architecture.
We present some examples of quantum algorithms proposed and implemented on a molecular spin qudit hardware.
arXiv Detail & Related papers (2024-05-31T16:43:20Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - A Quantum Algorithm Based Heuristic to Hide Sensitive Itemsets [1.8419202109872088]
We present a quantum approach to solve a well-studied problem in the context of data sharing.
We present results on experiments involving small datasets to illustrate how the problem could be solved using quantum algorithms.
arXiv Detail & Related papers (2024-02-12T20:44:46Z) - 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 Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - Systematic Literature Review: Quantum Machine Learning and its
applications [0.0]
This manuscript aims to present a Systematic Literature Review of the papers published between 2017 and 2023.
This study identified 94 articles that used quantum machine learning techniques and algorithms.
An improvement in the quantum hardware is required since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.
arXiv Detail & Related papers (2022-01-11T17:36:34Z) - Parametrized Complexity of Quantum Inspired Algorithms [0.0]
Two promising areas of quantum algorithms are quantum machine learning and quantum optimization.
Motivated by recent progress in quantum technologies and in particular quantum software, research and industrial communities have been trying to discover new applications of quantum algorithms.
arXiv Detail & Related papers (2021-12-22T06:19:36Z) - Quantum Machine Learning For Classical Data [0.0]
We study the intersection of quantum computing and supervised machine learning algorithms.
In particular, we investigate what extent quantum computers can be used to accelerate supervised machine learning algorithms.
arXiv Detail & Related papers (2021-05-08T12:11:44Z) - Quantum walk processes in quantum devices [55.41644538483948]
We study how to represent quantum walk on a graph as a quantum circuit.
Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
arXiv Detail & Related papers (2020-12-28T18:04:16Z)
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.