A Novel Image Classification Framework Based on Variational Quantum Algorithms

TL;DR

This work introduces a novel image classification framework that leverages variational quantum algorithms (VQAs) hybrid approaches combining quantum and classical computing paradigms within quantum machine learning and demonstrates the superiority of the proposed quantum architecture over its classical counterpart through a series of state vector simulation experiments on public datasets.

Abstract

Image classification is a crucial task in machine learning with widespread practical applications. The existing classical framework for image classification typically utilizes a global pooling operation at the end of the network to reduce computational complexity and mitigate overfitting. However, this operation often results in a significant loss of information, which can affect the performance of classification models. To overcome this limitation, we introduce a novel image classification framework that leverages variational quantum algorithms (VQAs)-hybrid approaches combining quantum and classical computing paradigms within quantum machine learning. The major advantage of our framework is the elimination of the need for the global pooling operation at the end of the network. In this way, our approach preserves more discriminative features and fine-grained details in the images, which enhances classification performance. Additionally, employing VQAs enables our framework to have fewer parameters than the classical framework, even in the absence of global pooling, which makes it more advantageous in preventing overfitting. We apply our method to different state-of-the-art image classification models and demonstrate the superiority of the proposed quantum architecture over its classical counterpart through a series of experiments on public datasets. Our experiments show that the proposed quantum framework achieves up to a 9.21% increase in accuracy and up to a 15.79% improvement in F1 score, compared to the classical framework.

Figures (5)

• Figure 1: Examples of two types of pooling operations. (a) Average pooling with a pooling area of size 2x2 and stride of 2. (b) Max pooling with a pooling area of size 2x2 and stride of 2.
• Figure 2: Examples of two types of global pooling operations. (a) Global average pooling. (b) Global max pooling.
• Figure 3: An example of a variational quantum circuit. The classical input data $x$ is encoded into a quantum state by the encoding module $E(x)$. This encoded state is then transformed by the following parameterized quantum circuit $U(\theta)$, namely the parameterized module, where $\theta$ are learnable parameters. Finally, the decoding module extracts classical information from the final quantum state by performing quantum measurements.
• Figure 4: Comparison of two image classification frameworks. (a) Classical framework. The input images are transformed by a backbone model into relevant features. The following global pooling module downsample these feature maps and outputs a fix-length vector which is fed into fully connected layers for the final classification. (b) Proposed framework. The global pooling module is replaced by a variational quantum circuit (VQC) with amplitude encoding which we denote by AE-VQC. The feature maps extracted by the backbone model are directly fed into this AE-VQC without dimensionality reduction. The outputs of the AE-VQC are transformed into classification results via fully connected layers.
• Figure 5: Two types of ansatzes used in this work. The single-qubit gate $R_i(\theta)$ represents a rotation around the $i$ axis of the Bloch sphere by an angle of $\theta$, where $i \in \{x,y,z\}$, and $\theta \in [0,2\pi)$ is a trainable parameter. Ansatz 2 contains two-qubit CNOT gates which might create quantum entanglement. The dashed box indicates a single circuit layer that can be repeated D times to enhance the expressive power of the ansatz.