Quantum Computing in AI Workloads
The scale that artificial intelligence applications have reached in recent years is exposing the limits of classical computing architectures, creating substantial resource demands particularly in large language models, deep learning networks, and high-dimensional optimization problems.
In this technical article prepared by our colleague Burak Çelik, how the superposition and entanglement properties of qubits open up new domains of speed and efficiency in AI workloads is examined through theoretical foundations, algorithmic approaches, NISQ-era practices, and hybrid quantum–classical architectures.
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