FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features

• URL: http://arxiv.org/abs/2209.08727v1
• Date: Mon, 19 Sep 2022 02:53:33 GMT
• Title: FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features
• Authors: Hankyul Baek, Won Joon Yun and Joongheon Kim
• Abstract summary: As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus. We propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits.
• Score: 12.261689483681145
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).

Related papers

• 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)
• 3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications [10.90994913062223]
  A quantum convolutional neural network (QCNN) is proposed for point cloud data processing in classification applications. A novel 3D scalable QCNN (sQCNN-3D) is proposed for point cloud data processing in classification applications.
  arXiv  Detail & Related papers  (2022-10-18T10:14:03Z)
• QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation [9.760266670459446]
  QuCNN is a parameterised multi-quantum-state based neural network layer computing similarities between each quantum filter state and each quantum data state. Back propagation can be achieved through a single-ancillabit quantum routine. validated by applying a convolutional layer with a data state and a filter state over a small subset of MNIST images.
  arXiv  Detail & Related papers  (2022-10-11T13:36:15Z)
• Scalable Quantum Convolutional Neural Networks [12.261689483681145]
  We propose a new version of quantum neural network (QCNN) named scalable quantum convolutional neural network (sQCNN) In addition, using the fidelity of QC, we propose an sQCNN training algorithm named reverse fidelity training (RF-Train) that maximizes the performance of sQCNN.
  arXiv  Detail & Related papers  (2022-09-26T02:07:00Z)
• Power and limitations of single-qubit native quantum neural networks [5.526775342940154]
  Quantum neural networks (QNNs) have emerged as a leading strategy to establish applications in machine learning, chemistry, and optimization. We formulate a theoretical framework for the expressive ability of data re-uploading quantum neural networks.
  arXiv  Detail & Related papers  (2022-05-16T17:58:27Z)
• QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks [71.14713348443465]
  We introduce a trainable quantum tensor network (QTN) for quantum embedding on a variational quantum circuit (VQC) QTN enables an end-to-end parametric model pipeline, namely QTN-VQC, from the generation of quantum embedding to the output measurement. Our experiments on the MNIST dataset demonstrate the advantages of QTN for quantum embedding over other quantum embedding approaches.
  arXiv  Detail & Related papers  (2021-10-06T14:44:51Z)
• Quantum convolutional neural network for classical data classification [0.8057006406834467]
  We benchmark fully parameterized quantum convolutional neural networks (QCNNs) for classical data classification. We propose a quantum neural network model inspired by CNN that only uses two-qubit interactions throughout the entire algorithm.
  arXiv  Detail & Related papers  (2021-08-02T06:48:34Z)
• A quantum algorithm for training wide and deep classical neural networks [72.2614468437919]
  We show that conditions amenable to classical trainability via gradient descent coincide with those necessary for efficiently solving quantum linear systems. We numerically demonstrate that the MNIST image dataset satisfies such conditions. We provide empirical evidence for $O(log n)$ training of a convolutional neural network with pooling.
  arXiv  Detail & Related papers  (2021-07-19T23:41:03Z)
• Toward Trainability of Quantum Neural Networks [87.04438831673063]
  Quantum Neural Networks (QNNs) have been proposed as generalizations of classical neural networks to achieve the quantum speed-up. Serious bottlenecks exist for training QNNs due to the vanishing with gradient rate exponential to the input qubit number. We show that QNNs with tree tensor and step controlled structures for the application of binary classification. Simulations show faster convergent rates and better accuracy compared to QNNs with random structures.
  arXiv  Detail & Related papers  (2020-11-12T08:32:04Z)
• Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition [101.69873988328808]
  We build upon a quantum convolutional neural network (QCNN) composed of a quantum circuit encoder for feature extraction. An input speech is first up-streamed to a quantum computing server to extract Mel-spectrogram. The corresponding convolutional features are encoded using a quantum circuit algorithm with random parameters. The encoded features are then down-streamed to the local RNN model for the final recognition.
  arXiv  Detail & Related papers  (2020-10-26T03:36:01Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

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.