Benchmarking Quantum Convolutional Neural Networks for Classification and Data Compression Tasks

• URL: http://arxiv.org/abs/2411.13468v1
• Date: Wed, 20 Nov 2024 17:17:09 GMT
• Title: Benchmarking Quantum Convolutional Neural Networks for Classification and Data Compression Tasks
• Authors: Jun Yong Khoo, Chee Kwan Gan, Wenjun Ding, Stefano Carrazza, Jun Ye, Jian Feng Kong,
• Abstract summary: Quantum Convolutional Neural Networks (QCNNs) have emerged as promising models for quantum machine learning tasks. This paper investigates the performance of QCNNs in comparison to the hardware-efficient ansatz (HEA) for classifying the phases of quantum ground states.
• Score: 0.4379805041989628
• License:
• Abstract: Quantum Convolutional Neural Networks (QCNNs) have emerged as promising models for quantum machine learning tasks, including classification and data compression. This paper investigates the performance of QCNNs in comparison to the hardware-efficient ansatz (HEA) for classifying the phases of quantum ground states of the transverse field Ising model and the XXZ model. Various system sizes, including 4, 8, and 16 qubits, through simulation were examined. Additionally, QCNN and HEA-based autoencoders were implemented to assess their capabilities in compressing quantum states. The results show that QCNN with RY gates can be trained faster due to fewer trainable parameters while matching the performance of HEAs.

Related papers

• Federated Quantum-Train with Batched Parameter Generation [3.697453416360906]
  We introduce the Federated Quantum-Train (QT) framework, which integrates the QT model into federated learning. Our approach significantly reduces qubit usage from 19 to as low as 8 qubits while reducing generalization error.
  arXiv  Detail & Related papers  (2024-09-04T14:39:11Z)
• Quantum support vector machines for classification and regression on a trapped-ion quantum computer [9.736685719039599]
  We examine our quantum machine learning models, which are based on quantum support vector classification (QSVC) and quantum support vector regression (QSVR) We investigate these models using a quantum-circuit simulator, both with and without noise, as well as the IonQ Harmony quantum processor. For the classification tasks, the performance of our QSVC models using 4 qubits of the trapped-ion quantum computer was comparable to that obtained from noiseless quantum-circuit simulations.
  arXiv  Detail & Related papers  (2023-07-05T08:06:41Z)
• Variational Quantum Neural Networks (VQNNS) in Image Classification [0.0]
  This paper investigates how training of quantum neural network (QNNs) can be done using quantum optimization algorithms. In this paper, a QNN structure is made where a variational parameterized circuit is incorporated as an input layer named as Variational Quantum Neural Network (VQNNs) VQNNs is experimented with MNIST digit recognition (less complex) and crack image classification datasets which converge the computation in lesser time than QNN with decent training accuracy.
  arXiv  Detail & Related papers  (2023-03-10T11:24:32Z)
• 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)
• Quantum-inspired Complex Convolutional Neural Networks [17.65730040410185]
  We improve the quantum-inspired neurons by exploiting the complex-valued weights which have richer representational capacity and better non-linearity. We draw the models of quantum-inspired convolutional neural networks (QICNNs) capable of processing high-dimensional data. The performance of classification accuracy of the five QICNNs are tested on the MNIST and CIFAR-10 datasets.
  arXiv  Detail & Related papers  (2021-10-31T03:10:48Z)
• On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification [88.31717434938338]
  The hybrid QCNNs enrich the classical architecture of CNNs by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the Land Use and Land Cover (LULC) classification, chosen as an Earth Observation (EO) use case. The results of the multiclass classification prove the effectiveness of the presented approach, by demonstrating that the QCNN performances are higher than the classical counterparts.
  arXiv  Detail & Related papers  (2021-09-20T12:41:50Z)
• 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)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)
• 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.