Reflection Equivariant Quantum Neural Networks for Enhanced Image Classification

• URL: http://arxiv.org/abs/2212.00264v3
• Date: Tue, 19 Sep 2023 07:01:55 GMT
• Title: Reflection Equivariant Quantum Neural Networks for Enhanced Image Classification
• Authors: Maxwell T. West, Martin Sevior, Muhammad Usman
• Abstract summary: We build new machine learning models which explicitly respect the symmetries inherent in their data, so-called geometric quantum machine learning (GQML) We find that these networks are capable of consistently and significantly outperforming generic ansatze on complicated real-world image datasets.
• Score: 0.7232471205719458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning is among the most widely anticipated use cases for near-term quantum computers, however there remain significant theoretical and implementation challenges impeding its scale up. In particular, there is an emerging body of work which suggests that generic, data agnostic quantum machine learning (QML) architectures may suffer from severe trainability issues, with the gradient of typical variational parameters vanishing exponentially in the number of qubits. Additionally, the high expressibility of QML models can lead to overfitting on training data and poor generalisation performance. A promising strategy to combat both of these difficulties is to construct models which explicitly respect the symmetries inherent in their data, so-called geometric quantum machine learning (GQML). In this work, we utilise the techniques of GQML for the task of image classification, building new QML models which are equivariant with respect to reflections of the images. We find that these networks are capable of consistently and significantly outperforming generic ansatze on complicated real-world image datasets, bringing high-resolution image classification via quantum computers closer to reality. Our work highlights a potential pathway for the future development and implementation of powerful QML models which directly exploit the symmetries of data.

Related papers

• 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)
• Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning [0.5181797490530444]
  We implement methods for the efficient preparation of quantum states representing encoded image data using variational, genetic and matrix product state based algorithms. Results show that these methods can approximately prepare states to a level suitable for QML using circuits two orders of magnitude shallower than a standard state preparation implementation.
  arXiv  Detail & Related papers  (2023-09-18T01:49:36Z)
• Classical-to-Quantum Transfer Learning Facilitates Machine Learning with Variational Quantum Circuit [62.55763504085508]
  We prove that a classical-to-quantum transfer learning architecture using a Variational Quantum Circuit (VQC) improves the representation and generalization (estimation error) capabilities of the VQC model. We show that the architecture of classical-to-quantum transfer learning leverages pre-trained classical generative AI models, making it easier to find the optimal parameters for the VQC in the training stage.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• FAENet: Frame Averaging Equivariant GNN for Materials Modeling [123.19473575281357]
  We introduce a flexible framework relying on frameaveraging (SFA) to make any model E(3)-equivariant or invariant through data transformations. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling.
  arXiv  Detail & Related papers  (2023-04-28T21:48:31Z)
• Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification [83.20479832949069]
  Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Here we investigate the problem-dependent power of QCs on multi-class classification tasks. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
  arXiv  Detail & Related papers  (2022-12-29T10:46:40Z)
• 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)
• Subtleties in the trainability of quantum machine learning models [0.0]
  We show that gradient scaling results for Variational Quantum Algorithms can be applied to study the gradient scaling of Quantum Machine Learning models. Our results indicate that features deemed detrimental for VQA trainability can also lead to issues such as barren plateaus in QML.
  arXiv  Detail & Related papers  (2021-10-27T20:28:53Z)
• Equivariant vector field network for many-body system modeling [65.22203086172019]
  Equivariant Vector Field Network (EVFN) is built on a novel equivariant basis and the associated scalarization and vectorization layers. We evaluate our method on predicting trajectories of simulated Newton mechanics systems with both full and partially observed data.
  arXiv  Detail & Related papers  (2021-10-26T14:26:25Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Hybrid Quantum-Classical Graph Convolutional Network [7.0132255816377445]
  This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data. The proposed framework demonstrates an advantage over classical multilayer perceptron and convolutional neural networks in the aspect of number of parameters. In terms of testing accuracy, the QGCNN shows comparable performance to a quantum convolutional neural network on the same HEP dataset.
  arXiv  Detail & Related papers  (2021-01-15T16:02:52Z)
• Predicting toxicity by quantum machine learning [11.696069523681178]
  We develop QML models for predicting the toxicity of 221 phenols on the basis of quantitative structure activity relationship. Results suggest that our data encoding enhanced by quantum entanglement provided more expressive power than the previous ones.
  arXiv  Detail & Related papers  (2020-08-18T02:59:40Z)

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.