Quantum Transfer Learning for MNIST Classification Using a Hybrid Quantum-Classical Approach

• URL: http://arxiv.org/abs/2408.03351v2
• Date: Thu, 31 Jul 2025 16:45:54 GMT
• Title: Quantum Transfer Learning for MNIST Classification Using a Hybrid Quantum-Classical Approach
• Authors: Soumyadip Sarkar,
• Abstract summary: We implement a hybrid quantum-classical model for image classification that compresses MNIST digit images into a low-dimensional feature space.<n>An autoencoder compresses each $28times28$ image (784 pixels) into a 64-dimensional latent vector.<n>We map these features onto a 5-qubit quantum state.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We implement a hybrid quantum-classical model for image classification that compresses MNIST digit images into a low-dimensional feature space and then maps these features onto a 5-qubit quantum state. First, an autoencoder compresses each $28\times28$ image (784 pixels) into a 64-dimensional latent vector, preserving salient features of the digit with minimal reconstruction error. We further reduce the latent representation to 5 principal components using Principal Component Analysis (PCA), to match the 5 available qubits. These 5 features are encoded as rotation angles in a quantum circuit with 5 qubits. The quantum feature map applies single-qubit rotations ($R_y$ gates) proportional to the feature values, followed by a Hadamard gate and a cascade of entangling CNOT gates to produce a non-product entangled state. Measuring the 5-qubit state yields a 32-dimensional probability distribution over basis outcomes, which serves as a quantum-enhanced feature vector for classification. A classical neural network with a softmax output is then trained on these 32-dimensional quantum feature vectors to predict the digit class. We evaluate the hybrid model on the MNIST dataset and compare it to a purely classical baseline that uses the 64-dimensional autoencoder latent features for classification. The results show that the hybrid model can successfully classify digits, demonstrating the feasibility of integrating quantum computing in the classification pipeline, although its accuracy (about 75\% on test data) currently falls below the classical baseline (about 98\% on the same compressed data).

Related papers

• An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
  Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks.<n>We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings.<n>We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
  arXiv  Detail & Related papers  (2025-04-13T11:49:53Z)
• Quantum generative classification with mixed states [3.5212321067549994]
  We present a quantum generative multiclass classification strategy, called quantum generative classification (QGC)<n>This model uses a variational quantum algorithm to estimate the joint probability density function of features and labels of a data set by means of a mixed quantum state.<n>We show that the quantum generative classification algorithm can be viewed as a Gaussian mixture that reproduces a kernel Hilbert space of the training data.
  arXiv  Detail & Related papers  (2025-02-27T10:56:47Z)
• Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
  Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML) At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
  arXiv  Detail & Related papers  (2024-11-13T23:56:20Z)
• Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification [0.0]
  We present a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The model was trained over 20 epochs, achieving a perfect 100% accuracy on the Iris dataset test set on 16 epoch. This work contributes to the growing body of research on hybrid quantum-classical models and their applicability to real-world datasets.
  arXiv  Detail & Related papers  (2024-10-21T13:15:12Z)
• 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 Hamiltonian Embedding of Images for Data Reuploading Classifiers [0.9374652839580181]
  One of the first considerations is the design of the quantum machine learning model itself. Recent research has started questioning whether quantum advantage via speedup is the right goal for quantum machine learning. In this paper, we take an alternative approach by incorporating the design of classical deep learning algorithms to the design of quantum neural networks.
  arXiv  Detail & Related papers  (2024-07-19T06:31:22Z)
• Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor [56.089799129458875]
  We present the results of benchmarking a quantum processor based on trapped $171$Yb$+$ ions.<n>We perform a supervised binary classification on two types of datasets: small binary digit images and weighted graphs with a ring topology.
  arXiv  Detail & Related papers  (2024-06-17T18:20:51Z)
• Towards Efficient Quantum Hybrid Diffusion Models [68.43405413443175]
  We propose a new methodology to design quantum hybrid diffusion models. We propose two possible hybridization schemes combining quantum computing's superior generalization with classical networks' modularity.
  arXiv  Detail & Related papers  (2024-02-25T16:57:51Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• 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)
• 3D-QAE: Fully Quantum Auto-Encoding of 3D Point Clouds [71.39129855825402]
  Existing methods for learning 3D representations are deep neural networks trained and tested on classical hardware. This paper introduces the first quantum auto-encoder for 3D point clouds.
  arXiv  Detail & Related papers  (2023-11-09T18:58:33Z)
• Quantum machine learning for image classification [39.58317527488534]
  This research introduces two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era. A second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process.
  arXiv  Detail & Related papers  (2023-04-18T18:23:20Z)
• The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning [52.77024349608834]
  Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. Key issue is how to address the inherent non-linearity of classical deep learning. We introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning.
  arXiv  Detail & Related papers  (2022-12-22T16:06:24Z)
• Photonic Quantum Computing For Polymer Classification [62.997667081978825]
  Two polymer classes visual (VIS) and near-infrared (NIR) are defined based on the size of the polymer gaps. We present a hybrid classical-quantum approach to the binary classification of polymer structures.
  arXiv  Detail & Related papers  (2022-11-22T11:59:52Z)
• Continuous Variable Quantum MNIST Classifiers [0.0]
  Quantum neural network hybrid multiclassifiers are presented using the MNIST dataset. The total of eight different classifiers are built using 2,3,...,8 qumodes. On a truncated MNIST dataset of 600 samples, a 4 qumode hybrid classifier achieves 100% training accuracy.
  arXiv  Detail & Related papers  (2022-04-04T00:51:24Z)
• Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning [0.5999777817331318]
  We propose a quantum machine learning approach based on quantum convolutional neural networks for solving multiclass classification problems. We use the proposed approach to demonstrate the 4-class classification for the case of the MNIST dataset using eight qubits for data encoding and four acnilla qubits. Our results demonstrate comparable accuracy of our solution with classical convolutional neural networks with comparable numbers of trainable parameters.
  arXiv  Detail & Related papers  (2022-03-29T09:07:18Z)
• Multi-class quantum classifiers with tensor network circuits for quantum phase recognition [0.0]
  Network-inspired circuits have been proposed as a natural choice for variational quantum eigensolver circuits. We present numerical experiments on multi-class entanglements based on tree tensor network and multiscale renormalization ansatz circuits.
  arXiv  Detail & Related papers  (2021-10-15T21:55:13Z)
• Quantum Deformed Neural Networks [83.71196337378022]
  We develop a new quantum neural network layer designed to run efficiently on a quantum computer. It can be simulated on a classical computer when restricted in the way it entangles input states.
  arXiv  Detail & Related papers  (2020-10-21T09:46:12Z)

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.