Abstract

Quantum Autoencoders emerge as a revolutionary framework in the field of data compression and feature learning, harnessing the unique advantages of quantum computing to enhance classical methodologies. This chapter explores the architecture and operational principles of quantum autoencoders, detailing their ability to efficiently compress high-dimensional data while retaining essential features. By leveraging quantum superposition and entanglement, these autoencoders surpass traditional techniques, offering improved performance in data representation and reconstruction tasks. Applications across various domains, including image processing, natural language processing, and genomics, are examined to highlight the versatility and effectiveness of quantum autoencoders. Additionally, the chapter addresses the challenges posed by current quantum hardware limitations and outlines future research directions aimed at optimizing the performance of these innovative models. The findings indicate that quantum autoencoders hold significant potential to transform data processing landscapes, making them a pivotal area for future exploration in quantum machine learning.

Introduction

Quantum Autoencoders have emerged as a transformative approach in the realms of data compression and feature learning, bridging the gap between quantum computing and classical machine learning techniques [1]. By leveraging the principles of quantum mechanics, such as superposition and entanglement, quantum autoencoders provide a novel framework that can efficiently handle high-dimensional data [2,3,4]. The foundational concept revolves around encoding input data into quantum states, allowing for enhanced data representation and reconstruction [5]. As data continues to grow exponentially in various fields, the demand for effective methods to manage and analyze this information becomes increasingly critical [6,7]. Quantum autoencoders not only promise improved performance in these tasks but also open new avenues for exploration in quantum machine learning [8].

The architecture of quantum autoencoders was typically comprised of two main components: the encoder and the decoder [9,10]. The encoder's role was to compress input data into a lower-dimensional quantum state, capturing the essential features while discarding redundant information [11]. Conversely, the decoder reconstructs the original data from this compressed representation, ensuring that vital information was preserved [12]. Quantum gates and operations play a crucial role in this process, allowing the autoencoder to learn complex mappings between input and output data [13,14]. This dual structure enables quantum autoencoders to achieve significant reductions in dimensionality while maintaining high fidelity in data reconstruction [15]. The inherent advantages of quantum mechanics facilitate the exploration of intricate relationships within the data, making quantum autoencoders a valuable tool for addressing the challenges associated with high-dimensional datasets [16,17].