Abstract

In Very Large Scale Integration (VLSI) applications, the demand for enhanced processing speeds in nano-electronic circuits has become paramount for meeting the escalating performance expectations of modern electronic devices. With the continuous miniaturization of electronic components, nano-electronics has emerged as a pivotal technology, driving innovation in VLSI circuits. However, the shrinking feature sizes pose challenges related to power consumption and processing speeds. Current methodologies, such as traditional optimization algorithms, struggle to cope with the intricacies of nano-electronic circuits, necessitating the exploration of unconventional techniques. Conventional optimization methods often fall short in achieving optimal performance for nano-electronic circuits due to the complex interactions at the quantum level. The proposed method leverages Differential Quantum Evolution, a hybrid algorithm combining the strengths of quantum computing and evolutionary algorithms. This approach facilitates efficient exploration of the vast solution space inherent in nano-electronic circuits. By harnessing quantum principles, the algorithm aims to surpass the limitations of classical optimization techniques, providing unprecedented levels of efficiency and speed. The experiments showcase promising results, indicating a significant enhancement in processing speeds and power efficiency for nano-electronic circuits optimized using Differential Quantum Evolution. The achieved results demonstrate the feasibility and potential of this approach to address the existing challenges in VLSI applications.