Hybrid Quantum Machine Learning Assisted Classification of COVID-19 from Computed Tomography Scans

• URL: http://arxiv.org/abs/2310.02748v1
• Date: Wed, 4 Oct 2023 11:37:58 GMT
• Title: Hybrid Quantum Machine Learning Assisted Classification of COVID-19 from Computed Tomography Scans
• Authors: Leo S\"unkel, Darya Martyniuk, Julia J. Reichwald, Andrei Morariu, Raja Havish Seggoju, Philipp Altmann, Christoph Roch, Adrian Paschke
• Abstract summary: We apply a hybrid quantum machine learning approach to a practically relevant problem with real world-data. More specifically, we classify large CT-scans of the lung into COVID-19, CAP, or Normal. We discuss quantum image embedding as well as hybrid quantum machine learning and evaluate several approaches to quantum transfer learning with various quantum circuits and embedding techniques.
• Score: 0.8098766536552447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Practical quantum computing (QC) is still in its infancy and problems considered are usually fairly small, especially in quantum machine learning when compared to its classical counterpart. Image processing applications in particular require models that are able to handle a large amount of features, and while classical approaches can easily tackle this, it is a major challenge and a cause for harsh restrictions in contemporary QC. In this paper, we apply a hybrid quantum machine learning approach to a practically relevant problem with real world-data. That is, we apply hybrid quantum transfer learning to an image processing task in the field of medical image processing. More specifically, we classify large CT-scans of the lung into COVID-19, CAP, or Normal. We discuss quantum image embedding as well as hybrid quantum machine learning and evaluate several approaches to quantum transfer learning with various quantum circuits and embedding techniques.

Related papers

• LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder [7.945302052915863]
  A potential application of quantum machine learning is to harness the power of quantum computers for generating classical data. We propose LatentQGAN, a novel quantum model that uses a hybrid quantum-classical GAN coupled with an autoencoder.
  arXiv  Detail & Related papers  (2024-09-22T23:18:06Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Hybrid Classical-Quantum architecture for vectorised image classification of hand-written sketches [0.0]
  Quantum machine learning investigates how quantum phenomena can be exploited to learn data in an alternative way. Recent advances indicate that hybrid classical-quantum models can attain competitive performances at low architecture complexities. Here, we introduce vector-based representation of sketch drawings as a test-bed for QML models.
  arXiv  Detail & Related papers  (2024-07-08T21:51:20Z)
• 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)
• Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines [7.106829260811707]
  We present an approach to employ Quantum Annealing (QA) in image classification. We propose using annealing-based Quantum Boltzmann Machines as part of a hybrid quantum-classical pipeline. We find that our approach consistently outperforms its classical baseline in terms of test accuracy and AUC-ROC-Score.
  arXiv  Detail & Related papers  (2023-11-27T16:07:49Z)
• Hybrid quantum transfer learning for crack image classification on NISQ hardware [62.997667081978825]
  We present an application of quantum transfer learning for detecting cracks in gray value images. We compare the performance and training time of PennyLane's standard qubits with IBM's qasm_simulator and real backends.
  arXiv  Detail & Related papers  (2023-07-31T14:45:29Z)
• 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)
• 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)
• Efficient Discrete Feature Encoding for Variational Quantum Classifier [3.7576442570677253]
  Variational quantum classification (VQC) is one of such methods with possible quantum advantage. We introduce the use of quantum random-access coding (QRAC) to map discrete features efficiently into limited number of qubits for VQC. We experimentally show that QRAC can help speeding up the training of VQC by reducing its parameters via saving on the number of qubits for the mapping.
  arXiv  Detail & Related papers  (2020-05-29T04:43:14Z)

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.