The recent intersection between Quantitative Artificial Intelligence
(Quantum AI) and edge computing is providing real-time decision-making. This is
achieved by combining the distributed processing power of edge computing with the
optimization capabilities of quantum computation. Edge computing that allows
analyzing data anywhere from the source resulting in lower latency, less utilization of
bandwidth and higher security when compared to mainstream computing systems and
Quantum AI that is based on quantum mechanics and is a much powerful version of
Classical AI as they are much faster in their problem-solving techniques. This directly
aids to discretionary fields such as autonomous systems, smart cities, predictable
maintenance, fraud detection, and healthcare industry that requires pushing intelligence
decision for real-time. Nonetheless, this fusion is promising; however, it has several
issues, such as limitations of quantum hardware, computational noise, security issues,
and compatibility issues. The current strategies involve the development of quantumclassical computers and hybrid systems, as well as quantum-safe programming and
energy-efficient quantum computing for augmenting the applicability of Quantum AI
in the Edge domain. Advancements in quantum computing as well as development of
edge intelligence are being set to transform industries by creating high levels of
efficiency, security, and automation in real-time operations.
Keywords: Cloud integration, Data processing, Decision making, Edge devices, Edge intelligence, Edge networks, Quantum algorithms, Quantum artificial intelligence (QAI), Quantum machine learning (QML), Quantum security, Realtime analytics, Internet of things (IoT), Machine learning models, Real-time data processing, Smart devices.
