RGB Image Classification with Quantum Convolutional Ansaetze

• URL: http://arxiv.org/abs/2107.11099v1
• Date: Fri, 23 Jul 2021 09:38:59 GMT
• Title: RGB Image Classification with Quantum Convolutional Ansaetze
• Authors: Yu Jing, Yang Yang, Chonghang Wu, Wenbing Fu, Wei Hu, Xiaogang Li and Hua Xu
• Abstract summary: We propose two types of quantum circuit ansaetze to simulate convolution operations on RGB images. To the best of our knowledge, this is the first work of a quantum convolutional circuit to deal with RGB images effectively. We also investigate the relationship between the size of quantum circuit ansatz and the learnability of the hybrid quantum-classical convolutional neural network.
• Score: 18.379304679643436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid growth of qubit numbers and coherence times in quantum hardware technology, implementing shallow neural networks on the so-called Noisy Intermediate-Scale Quantum (NISQ) devices has attracted a lot of interest. Many quantum (convolutional) circuit ansaetze are proposed for grayscale images classification tasks with promising empirical results. However, when applying these ansaetze on RGB images, the intra-channel information that is useful for vision tasks is not extracted effectively. In this paper, we propose two types of quantum circuit ansaetze to simulate convolution operations on RGB images, which differ in the way how inter-channel and intra-channel information are extracted. To the best of our knowledge, this is the first work of a quantum convolutional circuit to deal with RGB images effectively, with a higher test accuracy compared to the purely classical CNNs. We also investigate the relationship between the size of quantum circuit ansatz and the learnability of the hybrid quantum-classical convolutional neural network. Through experiments based on CIFAR-10 and MNIST datasets, we demonstrate that a larger size of the quantum circuit ansatz improves predictive performance in multiclass classification tasks, providing useful insights for near term quantum algorithm developments.

Related papers

• 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)
• Quantum Transfer Learning for MNIST Classification Using a Hybrid Quantum-Classical Approach [0.0]
  This research explores the integration of quantum computing with classical machine learning for image classification tasks. We propose a hybrid quantum-classical approach that leverages the strengths of both paradigms. The experimental results indicate that while the hybrid model demonstrates the feasibility of integrating quantum computing with classical techniques, the accuracy of the final model, trained on quantum outcomes, is currently lower than the classical model trained on compressed features.
  arXiv  Detail & Related papers  (2024-08-05T22:16:27Z)
• Hybrid Quantum-Classical Neural Network for LAB Color Space Image Classification [1.565361244756411]
  Quantum convolutional neural networks (QCNNs) are structurally similar to classical convolutional neural networks. We show that two channels consistently exhibit higher classification accuracy in images from different color spaces than the third.
  arXiv  Detail & Related papers  (2024-06-04T11:46:56Z)
• 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)
• Quantum Convolutional Neural Networks for Multi-Channel Supervised Learning [0.0]
  We present a variety of hardware-adaptable quantum circuit ansatzes for use as convolutional kernels. We demonstrate that the quantum neural networks we report outperform existing QCNNs on classification tasks involving multi-channel data.
  arXiv  Detail & Related papers  (2023-05-30T11:48:12Z)
• Simulation of Entanglement Generation between Absorptive Quantum Memories [56.24769206561207]
  We use the open-source Simulator of QUantum Network Communication (SeQUeNCe), developed by our team, to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories. We realize the representation of photonic quantum states within truncated Fock spaces in SeQUeNCe. We observe varying fidelity with SPDC source mean photon number, and varying entanglement generation rate with both mean photon number and memory mode number.
  arXiv  Detail & Related papers  (2022-12-17T05:51:17Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• A hybrid quantum image edge detector for the NISQ era [62.997667081978825]
  We propose a hybrid method for quantum edge detection based on the idea of a quantum artificial neuron. Our method can be practically implemented on quantum computers, especially on those of the current noisy intermediate-scale quantum era.
  arXiv  Detail & Related papers  (2022-03-22T22:02:09Z)
• Experimental Quantum Generative Adversarial Networks for Image Generation [93.06926114985761]
  We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor. Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
  arXiv  Detail & Related papers  (2020-10-13T06:57:17Z)

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.