Quantum computing and generative adversarial networks are combined into a single architecture, quantum generative adversarial networks (QGANs), a powerful tool for data generation data generation and simulation data simulation . Applications of QGANs QGAN applications , for example, finance and healthcare healthcare (including cryptography cryptography ), are repeated in this chapter. In this chapter, we will see how a QGAN approach to problems can preserve data privacy and explore more challenging features of machine learning or optimization in solving complex problem sets. We will give readers an idea of the many different applications these machines have and where they may fall short – not surprisingly, the author looks at a broad range of industries.
Quantum computing and generative adversarial networks are combined into a single architecture, quantum generative adversarial networks (QGANs), a powerful tool for data generation data generation and simulation data simulation . Applications of QGANs QGAN applications , for example, finance and healthcare healthcare (including cryptography cryptography ), are repeated in this chapter. In this chapter, we will see how a QGAN approach to problems can preserve data privacy and explore more challenging features of machine learning or optimization in solving complex problem sets. We will give readers an idea of the many different applications these machines have and where they may fall short – not surprisingly, the author looks at a broad range of industries.
Quantum generative adversarial networks (QGANs) represent a significant advancement at the intersection of quantum computing and machine learning, offering novel approaches to data encoding and generation for next-generation quantum applications. This chapter provides a comprehensive examination of QGANs, exploring their fundamental architecture, implementation strategies, and potential applications. We detail how QGANs leverage quantum physics principles to potentially outperform classical generative adversarial networks (GANs), particularly in scenarios requiring quantum data processing and quantum-enhanced computations. The study outlines the critical differences between classical GANs and QGANs, focusing on their unique quantum circuit-based generator quantum circuit-based generator and discriminator components. We present an in-depth analysis of quantum data encoding techniques, including state representation, preparation methods, and various encoding scheme encoding scheme s such as amplitude, basis, and phase encoding. The chapter also addresses the challenges in QGAN implementation, discussing scalability issues, noise management, and resource optimization strategies. Special attention is given to training methodologies, including hybrid quantum-classical approaches and specialized loss functions for quantum systems. Furthermore, we explore practical applications across multiple domains, including quantum machine learning, quantum simulation, and quantum cryptography. Real-world case studies in financial modeling financial modeling , healthcare, and cybersecurity demonstrate the practical potential of QGANs. The chapter concludes by identifying future research directions and opportunities for advancing QGAN technology, emphasizing the importance of interdisciplinary collaboration in developing more sophisticated and practical quantum computing solutions.
Generative Adversarial Networks (GANs) are generative models that function as a minimax game, in which a generative network and a discriminative network compete against each other with the goal of creating data that convincingly resembles the real sample. With the advent of Quantum Computing and the development of Quantum Machine Learning (QML) models, Quantum Generative Adversarial Networks (QuGANs) have been increasingly studied due to the possible advantages this new type of architecture can offer, especially regarding performance improvements, scalability, and the exploration of new applications. In this context, this study's guiding question is: how were QuGANs developed between the years 2018 and 2025? To answer this question, the general objective of this work is to conduct a systematic literature review of QuGANs during the proposed period. Using a systematic literature review as the methodological basis, articles published and available on the online platforms Lens, Scopus, and Web of Science were selected, and the Rayyan tool was employed to identify duplicate works and those that did not specifically address QuGANs. As a result, a prevalence of hybrid models was observed, in which the developed architecture integrates quantum and classical characteristics in a complementary manner. Regarding the type of application, approaches involving theoretical foundations and image generation stand out as the most common. Other application areas are also explored (chemistry and pharmaceuticals, quantum error correction, high-energy physics, experimental implementation, medical applications, cloud computing, anomaly detection, telecommunications, biometrics, finance, physics and simulations, security and cryptography, software engineering, noise in QuGANs, and survey), demonstrating the broad potential of QuGANs across various research fields and industrial sectors.
Exploring the potential for collaboration between adversarial learning and quantum mechanics is crucial given the explosive expansion of quantum computers and the growing need for potent generative models. This chapter tackles developing intersection by introducing the idea of quantum generative adversarial networks (QGANs), a unique framework that combines the generative powers of GANs with quantum computing. The fundamentals of QGANs are presented, along with a comparison with classical GANs and an emphasis on their possible quantum advantage. Qubit encoding, parameterized gates, and entanglement techniques are covered in detail, including the quantum circuit design necessary for putting QGAN structures into practice. The training paradigms and optimization techniques unique to quantum environments, including hybrid quantum-classical approaches, are described. The significance of QGANs in fields including secure data synthesis, picture production, and quantum chemistry is illustrated through practical applications and case studies. The chapter ends by outlining important unresolved issues and suggesting future lines of inquiry to develop the subject of quantum generative modelling.
Quantum generative adversarial networks (QGANs) is a novel topical approach that enhances data creation and analysis by incorporating elements of quantum computing quantum computing (QC) into GANs. This chapter aims to look at the relatively young research area called QGANs, which combines the cutting-edge technology of quantum computing quantum computing (QC) with traditional generative modeling to reveal its potential in various fields. Thus, QGANs are expected to have the most profound impact on quantum chemistry, financial approaches, image processing, and many other fields, as they implement the quantum perspective on data generation by utilizing the potential of quantum structures. Some are asymptotic improvements, and others are algorithmic improvements for better stability and scalability using quantum hardware, and finding use in quantum computing quantum computing (QC) applications in various fields, such as pharmaceuticals or climate science. The advancement of QGAN can only be attributed to the advancement in quantum computing quantum computing (QC) hardware and the better ways of implementing both classical and quantum paradigms for enhancing performance. As with other AI technolog AI technology ies, there are significant ethical concerns over data privacy protection and possible bias within the generated data, not to mention relevant legislation of practical QGAN uses. Nevertheless, the described challenges do not undermine the benefits that QGANs may offer in the future to address problems that classical computing cannot solve on its own, thus opening a new era in data modeling. As further research and development advance the functions and functionality of QGANs, the readiness of QGANs for broader practical use is evident, thus opening up a transformative and luxurious era in the intersecting areas of quantum computing quantum computing (QC) and generative model generative model s.
This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) to data augmentation for machine learning. Traditional Generative Adversarial Networks (GANs) rely on classical neural networks, while this research explores leveraging quantum circuits as generators within a QGAN architecture. The core idea is to train a quantum generator to produce synthetic data samples, supplementing existing datasets with novel instances. The discriminator remains a classical neural network, tasked with distinguishing between real and generated data. We present a theoretical framework for this approach, outlining the mathematical formulation and key components. Specifically, we introduce the notation (x, s_1, s_2, ..., s_K) to represent data samples, generator states, and relevant parameters. The objective function for the QGAN is defined as minimizing the Jensen-Shannon divergence between the real and generated data distributions. The effectiveness of this approach is discussed, highlighting potential advantages and challenges. This work contributes to the emerging field of quantum machine learning and offers a novel strategy for improving the performance of machine learning models, particularly in scenarios with limited training data.
There has been a significant change in QAI with the introduction of Quantum Generative Adversarial Networks (QGAN), Quantum Variational GAN (QVGAN), Quantum-enhanced GAN (QEGAN), and Quantum Wasserstein GAN (QWGAN). QGAN brings the power of GANs into the quantum world, paving the way for the creation of quantum data distributions that can then be used to facilitate both quantum data augmentation and synthesis. QVGAN optimizes the parameters of quantum circuits by drawing on variational concepts to improve quantum generative modeling. QEGAN utilizes quantum hardware to speed up adversarial training, which in turn speeds up the production of quantum data. Aligning the distribution of quantum data is QWGAN's main goal, since this helps quantum datasets converge. Collectively, these quantum GAN variations open new possibilities for QAI by facilitating the production, manipulation, and alignment of quantum data distributions. QGANs and variations can break through classical barriers and push forward quantum data-driven applications in fields as diverse as quantum chemistry, quantum machine learning, and quantum cryptography by harnessing the power of quantum computing. QGAN variants' quantum benefits highlight their importance in pushing the limits of quantum information processing.