Overcoming Challenges in Implementing Quantum AI

Marco Baioletti, Fabrizio Fagiolo, Corrado Appice Annalisa Malerba Donato Loglisci +4 more

In the twenty-first century, humanity is compelled to face global challenges. Such challenges involve complex systems. However, science has some cognitive and predictive limits in dealing with complex systems. Some of these limits are related to computational complexity and the recognition of variable patterns. To overcome these limits, artificial intelligence (AI) and quantum computing (QC) appear to be helpful. Even more promising is quantum AI (QAI), which emerged from the combination of AI and QC. The combination of AI and QC produces reciprocal, synergistic effects. This work describes some of these effects. It shows that QC offers new materials for implementing AI and innovative algorithms for solving optimisation problems and enhancing machine learning algorithms. Additionally, it demonstrates how AI algorithms can help overcome many of the experimental challenges associated with implementing QC. It also outlines several perspectives for the future development of quantum artificial intelligence.

Maha A. Metawei, Hesham E. ElDeeb, Salwa M. Nassar +1 more

Madhusudan Singh Irish Singh, Bharat S. Rawal

Fadi M. Al-Turjman, Ramiz Salama

This study examines successful AI and Pall implementations across various industries, focusing on how these technologies handle specific challenges and provide noticeable outcomes. It examines AI models like natural language processing and machine literacy, as well as platforms like Microsoft Azure and Amazon Web Services. The success cases highlight key elements for successful integration, including leadership, change operations, data management, and data cleansing. The study also examines how companies overcome data cleansing, governance, and security issues to ensure reliable AI systems powered by reliable sources. The investigation will explore how AI models are linked to enterprise systems, focusing on security measures and data encryption. Case studies will analyze data protection features, providing valuable insights into AI’s limitations. The goal is to present a comprehensive understanding of AI integration, highlighting challenges and strategies used to overcome them. By showcasing real-world examples, the design aims to inspire businesses to harness AI’s transformative potential, paving the way for growth and innovation.

A. Vasantharaj, Mallikarjuna Rao Gundavarapu

The convergence of quantum computing and edge artificial intelligence (AI) represents a transformative advancement in real-time decision-making capabilities across diverse domains. This chapter explores the intricate relationship between these two cutting-edge technologies, emphasizing their synergistic potential to enhance urban management, healthcare, and industrial automation. By leveraging quantum computing's computational prowess alongside the low-latency processing capabilities of edge AI, organizations can address complex challenges and optimize operations in dynamic environments. The discussion delves into current quantum technologies relevant to edge AI, the architectural considerations for successful implementation, and the potential applications that can benefit from this integration. Additionally, significant technical challenges, such as hardware limitations, algorithm development, and security concerns, are examined, along with future directions to overcome these barriers. Ultimately, this chapter highlights the promising future of quantum edge AI as a pivotal solution for enhancing efficiency, sustainability, and responsiveness in the modern landscape.