Distributed Quantum Neural Networks for Scalable Machine Learning

In this article, we propose a new approach to handwritten digit recognition using Distributed Quantum Neural Networks (DQNNs). Our method leverages the power of quantum computing to improve the accuracy and efficiency of handwritten digit recognition tasks.
To understand how DQNNs work, let’s start with a classic example: trying to recognize handwritten digits. Imagine you have a large dataset of images of handwritten digits, each one represented by a set of numbers that describe its features (like the shape of the digit and the placement of lines). Our goal is to develop a machine learning model that can take these feature representations as input and output the correct digit label.
Traditional neural networks are great at handling small datasets, but they struggle when dealing with large ones. This is where DQNNs come in – they’re designed to handle large datasets by breaking them down into smaller parts and processing each part using multiple quantum bits (qubits). In essence, DQNNs use quantum parallelism to speed up the feature extraction process, allowing them to recognize handwritten digits more accurately and quickly than traditional neural networks.
But how do we build these DQNNs? The architecture of a QNN consists of multiple layers of quantum gates (like rotation gates and CZ gates) that manipulate qubits in a specific way. Each layer processes the input data differently, allowing the model to extract different features from the data. By stacking multiple layers, we can capture more complex patterns in the data, leading to improved recognition accuracy.
We tested our DQNNs on two popular datasets: Semeion and MNIST. On the Semeion dataset, which contains 1593 images of handwritten digits, our DQNNs achieved an accuracy of 97.4%, outperforming traditional neural networks. On the MNIST dataset, which contains 60,000 images of handwritten digits, our DQNNs achieved an accuracy of 98.5%, also surpassing traditional neural networks.
In summary, Distributed Quantum Neural Networks offer a powerful tool for handwritten digit recognition tasks, leveraging the quantum parallelism property to improve accuracy and efficiency. By breaking down large datasets into smaller parts and processing each part using multiple qubits, DQNNs can capture complex patterns in the data more effectively than traditional neural networks. Our experiments show that DQNNs outperform traditional neural networks on both Semeion and MNIST datasets, demonstrating their potential for practical applications in image recognition tasks.