Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines

• URL: http://arxiv.org/abs/2310.10910v1
• Date: Tue, 17 Oct 2023 01:06:59 GMT
• Title: Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines
• Authors: Davut Emre Tasar, Kutan Koruyan, Ceren Ocal Tasar
• Abstract summary: This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. We scrutinize the classification prowess of classical SVM and Quantum Support Vector Machines operational on quantum hardware over the Iris dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. Particularly, by emphasizing on Support Vector Machines (SVM), we scrutinize the classification prowess of classical SVM and Quantum Support Vector Machines (QSVM) operational on quantum hardware over the Iris dataset. The methodology embraced encapsulates an extensive array of experiments orchestrated through the Qiskit library, alongside hyperparameter optimization. The findings unveil that in particular scenarios, QSVMs extend a level of accuracy that can vie with classical SVMs, albeit the execution times are presently protracted. Moreover, we underscore that augmenting quantum computational capacity and the magnitude of parallelism can markedly ameliorate the performance of quantum machine learning algorithms. This inquiry furnishes invaluable insights regarding the extant scenario and future potentiality of machine learning applications in the quantum epoch. Colab: https://t.ly/QKuz0

Related papers

• Quantum Machine Learning with Application to Progressive Supranuclear Palsy Network Classification [0.0]
  We present a quantum machine learning model for the diagnosis of Progressive Supranuclear Palsy (PSP) disorder. The results suggest that quantum machine learning has led to noticeable advancement and outperforms classical frameworks. In particular, we have demonstrated the successful application of the present model on both a quantum simulator and real chips of the IBM quantum platform.
  arXiv  Detail & Related papers  (2024-07-06T14:16:31Z)
• Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavour Tagging [0.7232471205719458]
  We develop and apply quantum machine learning methods to B meson tagging. We simulate boosted ensembles of quantum support vector machines based on both conventional qubit-based and continuous variable architectures. We find evidence that continuous variable QSVMs beyond the classically simulable regime may be able to realise even higher performance.
  arXiv  Detail & Related papers  (2023-05-04T11:04:06Z)
• A Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification [26.80167258721593]
  This work proposes a novel quantum SVM for direct multiclass classification based on quantum annealing, called Quantum Multiclass SVM (QMSVM) The main objective of this work is to evaluate the feasibility, accuracy, and time performance of this approach. Experiments have been performed on the D-Wave Advantage quantum annealer for a classification problem on remote sensing data.
  arXiv  Detail & Related papers  (2023-03-21T09:51:19Z)
• TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework [59.07246314484875]
  TeD-Q is an open-source software framework for quantum machine learning. It seamlessly integrates classical machine learning libraries with quantum simulators. It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
  arXiv  Detail & Related papers  (2023-01-13T09:35:05Z)
• 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)
• 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)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• Quantum Support Vector Machines for Continuum Suppression in B Meson Decays [0.27342795342528275]
  We investigate the effect of different quantum encoding circuits, the process that transforms classical data into a quantum state, on the final classification performance. We show an encoding approach that achieves an average Area Under Receiver Operating Characteristic Curve (AUC) of 0.848 determined using quantum circuit simulations. Using a reduced version of the dataset we then ran the algorithm on the IBM Quantum ibmq_casablanca device achieving an average AUC of 0.703.
  arXiv  Detail & Related papers  (2021-03-23T02:09:05Z)
• Facial Expression Recognition on a Quantum Computer [68.8204255655161]
  We show a possible solution to facial expression recognition using a quantum machine learning approach. We define a quantum circuit that manipulates the graphs adjacency matrices encoded into the amplitudes of some appropriately defined quantum states.
  arXiv  Detail & Related papers  (2021-02-09T13:48:00Z)
• QEML (Quantum Enhanced Machine Learning): Using Quantum Computing to Enhance ML Classifiers and Feature Spaces [0.49841205356595936]
  Machine learning and quantum computing are causing a paradigm shift in the performance and behavior of certain algorithms. This paper first understands the mathematical intuition for the implementation of quantum feature space. We build a noisy variational quantum circuit KNN which mimics the classification methods of a traditional KNN.
  arXiv  Detail & Related papers  (2020-02-22T04:14:32Z)

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.