329Chapter 17 Quantum GANs: Revolutionizing data encoding for next-generation quantum applications
Samaya Pillai, Pankaj Pathak, N. A. Natraj +2 more
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.
De Gruyter eBooks•Aug 9, 2025•0 citations