Quantum Neural Networks for Classifying Large-Scale Data

Tak Hur, Leeseok Kim, Daniel K. Park

Shiwen An, Konstantinos Slavakis

Quantum computing has been a prominent research area for decades, inspiring transformative fields such as quantum simulation, quantum teleportation, and quantum machine learning (QML), which are undergoing rapid development. Within QML, hybrid classical-quantum algorithms like Quantum Neural Networks (QNNs) and Variational Quantum Classifiers (VQCs) have shown promise in leveraging quantum circuits and classical optimizers to classify classical data efficiently. Simultaneously, classical machine learning has made significant strides in graph classification, employing Graph Neural Networks (GNNs) to analyze systems ranging from large-scale structures like the Large Hadron Collider to molecular and biological systems like proteins and DNA. Combining the advancements in quantum computing and graph classification presents a unique opportunity to develop quantum algorithms capable of extracting features from graphs and performing their classification effectively. In this paper, we propose a novel quantum encoding framework for graph classification using VQCs. Unlike existing approaches such as PCA-VQC, which rely on dimensionality reduction techniques like Principal Component Analysis (PCA) and may lead to information loss, our method preserves the integrity of graph data. Furthermore, our encoding approach is optimized for Noise-Intermediate Scale Quantum (NISQ) devices, requiring a limited number of qubits while achieving comparable or superior classification performance to PCA-VQC. By constructing slightly more complex circuits tailored for graph encoding, we demonstrate that VQCs can effectively classify graphs within the constraints of current quantum hardware.

Amine Zeguendry, Zahi Jarir, Mohamed Quafafou

Machine learning (ML) is often listed among the most potential applications for quantum computing. This is in reality a puzzling choice: Nowadays machine learning algorithms are extremely powerful in practice, but still theoretically challenging to understand. Quantum computing, in contrast, does not give actual benchmarks on practical scales. However, theory remains the main tool we have to determine if it may become important for a task. Although it is far from reaching its full potential, nevertheless, it presents several techniques to efficiently deal with the problem which are tough to solve traditionally. In this perspective we deploy the Quanvolutional Neural Networks (QNNs) on a quantum computer to recognize handwritten digits, and compare its performance with that of its classical counterpart namely the Convolutional Neural Networks (CNNs). We used a quanvolutional layer to separate the pixel data and demonstrate the advantage of applying QNN on a quantum computer. Finally, we implement the Variational Quantum Classifier (VQC) and several classical classifiers built on the Iris dataset, in order to compare their accuracies. We employed amplitude encoding to map data into the large Hilbert space of a quantum computer. The results revealed that the QNN model and VQC had high accuracy and low loss value compared to their purely classical counterparts.

Davide Pastorello

R. Kretzschmar, R. Bueler, N.B. Karayiannis +1 more

This study investigates the capacity of quantum neural networks (QNNs) to function as fuzzy classifiers. For this purpose, QNNs are compared with multilayer feedforward neural networks (FFNNs). The experiments are performed on two-dimensional speech data and investigate a variety of issues involved in the training of QNNs. This experimental study verifies that QNNs are capable of representing and quantifying the uncertainty inherent in the training data. It is also shown that simple post-processing of the QNN outputs makes QNNs an attractive alternative to conventional FFNNs for pattern classification applications.

Yuxuan Du, Xinbiao Wang, Naixu Guo +6 more