I n modern technology, artificial intelligence (AI) has advanced from theoretical algorithms into practical systems that process complex multimodal data, model natural language, and evaluate high-dimensional visual inputs. As deep learning models scale, traditional silicon-based architectures face severe physical and thermodynamic constraints, including heat dissipation limits, interconnect latency, and the memory wall. To overcome these bottlenecks, the technology industry is pursuing specialized solutions. While novel silicon architectures, chiplet designs, and photonic computing address immediate AI scaling demands, quantum computing is emerging in parallel to tackle fundamentally intractable optimization and simulation problems that lie beyond classical reach.