Quantum algorithms use in trust-centric AI applications

The realm of quantum computing is experiencing rapid growth, harnessing the principles of quantum mechanics to exponentially accelerate computations that surpass classical computing capabilities. Such advancements can potentially reshape the landscape of reliable artificial intelligence, particularly in the context of data-intensive, intricate decision-making processes. Diverse trust-oriented AI frameworks have been introduced to address various AI applications. This paper evaluates multiple AI systems focused on trust, compiling observations about their corresponding quantum algorithms. The analysis delves into quantum algorithmic approaches within three key trust-centric AI areas: detecting fraudulent users in social networks, creating diagnostic systems for healthcare, and optimising pathways for trust aggregation in social networks. This study unveils a pivotal observation: quantum algorithms demonstrate diminished time complexity, enhancing trust-based AI applications' expeditiousness.