Trainable Quantum Neural Network for Multiclass Image Classification with the Power of Pre-trained Tree Tensor Networks

• URL: http://arxiv.org/abs/2504.14995v1
• Date: Mon, 21 Apr 2025 09:51:39 GMT
• Title: Trainable Quantum Neural Network for Multiclass Image Classification with the Power of Pre-trained Tree Tensor Networks
• Authors: Keisuke Murota, Takumi Kobori,
• Abstract summary: Tree tensor networks (TTNs) offer powerful models for image classification.<n> embedding TTNs into quantum neural networks (QNNs) may further improve the performance by leveraging quantum resources.<n>We propose forest tensor network (FTN)-classifiers, which aggregate multiple small-bond-dimension TTNs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tree tensor networks (TTNs) offer powerful models for image classification. While these TTN image classifiers already show excellent performance on classical hardware, embedding them into quantum neural networks (QNNs) may further improve the performance by leveraging quantum resources. However, embedding TTN classifiers into QNNs for multiclass classification remains challenging. Key obstacles are the highorder gate operations required for large bond dimensions and the mid-circuit postselection with exponentially low success rates necessary for the exact embedding. In this work, to address these challenges, we propose forest tensor network (FTN)-classifiers, which aggregate multiple small-bond-dimension TTNs. This allows us to handle multiclass classification without requiring large gates in the embedded circuits. We then remove the overhead of mid-circuit postselection by extending the adiabatic encoding framework to our setting and smoothly encode the FTN-classifiers into a quantum forest tensor network (qFTN)- classifiers. Numerical experiments on MNIST and CIFAR-10 demonstrate that we can successfully train FTN-classifiers and encode them into qFTN-classifiers, while maintaining or even improving the performance of the pre-trained FTN-classifiers. These results suggest that synergy between TTN classification models and QNNs can provide a robust and scalable framework for multiclass quantum-enhanced image classification.

Related papers

• Adiabatic Encoding of Pre-trained MPS Classifiers into Quantum Circuits [0.0]
  We propose a framework that encodes pre-trained MPS-classifiers into quantum MPS circuits with postselection, and gradually removes the postselection while retaining performance.<n>We prove that training qMPS-classifiers from scratch on a certain artificial dataset is exponentially hard due to barren plateaus, but our adiabatic encoding circumvents this issue.
  arXiv  Detail & Related papers  (2025-04-12T15:12:46Z)
• Multi-Class Quantum Convolutional Neural Networks [2.6422127672474933]
  We propose a quantum convolutional neural network (QCNN) for multi-class classification of classical data. The QCNN is tested on the MNIST dataset with 4, 6, 8 and 10 classes.
  arXiv  Detail & Related papers  (2024-04-19T09:36:48Z)
• Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits [70.97518416003358]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices. While tensor-train networks (TTNs) can enhance VQC representation and generalization, the resulting hybrid model, TTN-VQC, faces optimization challenges due to the Polyak-Lojasiewicz (PL) condition. To mitigate this challenge, we introduce Pre+TTN-VQC, a pre-trained TTN model combined with a VQC.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• 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)
• Hybrid Graph Neural Networks for Few-Shot Learning [85.93495480949079]
  Graph neural networks (GNNs) have been used to tackle the few-shot learning problem. Under the inductive setting, existing GNN based methods are less competitive. We propose a novel hybrid GNN model consisting of two GNNs, an instance GNN and a prototype GNN.
  arXiv  Detail & Related papers  (2021-12-13T10:20:15Z)
• 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)
• Hybrid quantum-classical classifier based on tensor network and variational quantum circuit [0.0]
  We introduce a hybrid model combining the quantum-inspired tensor networks (TN) and the variational quantum circuits (VQC) to perform supervised learning tasks. We show that a matrix product state based TN with low bond dimensions performs better than PCA as a feature extractor to compress data for the input of VQCs in the binary classification of MNIST dataset.
  arXiv  Detail & Related papers  (2020-11-30T09:43:59Z)
• 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)
• Block-term Tensor Neural Networks [29.442026567710435]
  We show that block-term tensor layers (BT-layers) can be easily adapted to neural network models, such as CNNs and RNNs. BT-layers in CNNs and RNNs can achieve a very large compression ratio on the number of parameters while preserving or improving the representation power of the original DNNs.
  arXiv  Detail & Related papers  (2020-10-10T09:58:43Z)
• A Fully Tensorized Recurrent Neural Network [48.50376453324581]
  We introduce a "fully tensorized" RNN architecture which jointly encodes the separate weight matrices within each recurrent cell. This approach reduces model size by several orders of magnitude, while still maintaining similar or better performance compared to standard RNNs.
  arXiv  Detail & Related papers  (2020-10-08T18:24:12Z)
• Quantum-Classical Machine learning by Hybrid Tensor Networks [14.851178989158976]
  We propose the quantum-classical hybrid networks (HTN) which combine tensor networks with classical neural networks in a uniform deep learning framework. We show the potential applications of HTN, including quantum classification and quantum autoencoder.
  arXiv  Detail & Related papers  (2020-05-15T10:20:35Z)

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.