# Synthesis and Optimization of Multi-Objective Multi-Output QCA Circuit using Genetic Algorithm

## TL;DR

This paper introduces a multi-objective genetic algorithm approach for synthesizing optimized multi-output QCA circuits, significantly reducing delay and gate count compared to existing methods.

## Contribution

It presents a novel methodology for efficient QCA circuit synthesis that minimizes delay and gate count using multi-objective genetic algorithms.

## Key findings

- Outperforms existing techniques in delay reduction
- Reduces gate count effectively
- Achieves optimal QCA circuit synthesis

## Abstract

The physical limitations of CMOS technology triggered several research for finding an alternative technology. QCA is one of the emerging nanotechnologies which is gaining attention as a substitute of CMOS. The main potential of QCA is its ultra low power consumption, less area overhead, and high speed. Majority and inverter gates are the basic gates in QCA,which together works as a universal logic gate to implement any QCA circuit. This paper proposes an efficient methodology for optimal QCA circuit synthesis of arbitrary multi-output boolean functions. A multi-objective genetic algorithm based approach is used to reduce worst case delay and gate count of a QCA circuit. Different importance is given to worst case delay, no. of majority and no. of inverter gates. Several efficient techniques are used in order to achieve the optimal result and reduce the computational complexity furthermore additional methodologies are used to eliminate redundancies from the final solution. Comparison of the obtained results with the existing best techniques indicates, the proposed technique outperforms in terms of worst case delay and gate count.