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Lecture notes on data engineering and communications technologies

Quantum Algorithms for Trust-Based AI Applications

Davinder Kaur, Süleyman Uslu, Arjan Durresi A

Published Jan 1, 2023• 4 citations
Publisher
DOI
10.1007/978-3-031-35734-3_1

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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.
Quantum circuit optimization is crucial for realizing the full potential of quantum computing, but current optimization techniques often operate as black boxes, lacking transparency and hindering trust. This paper proposes a novel approach combining tensor networks with explainable AI to address this challenge. We represent quantum circuits using tensor networks, allowing for a richer mathematical representation and facilitating the application of explainable AI methods. The developed system generates visualizations and textual explanations detailing the key factors driving optimization decisions, promoting understanding and trust in the process. The core claim highlights the critical need for explainability in AI-driven optimization, while the proposed mechanism leverages tensor networks to achieve this. We demonstrate the potential of this combined approach to enhance transparency and accelerate the adoption of quantum circuit optimization techniques. This work introduces a new paradigm for quantum optimization, moving beyond opaque algorithms towards interpretable and trustworthy solutions.