  In our pursuit of quantum supremacy during the NISQ era, this research
introduces a novel approach rooted in the Quantum Approximate Optimization
Algorithm (QAOA) framework to address the Traveling Salesman Problem (TSP). By
strategically reducing the requisite qubit count from $n^2$ to $n\log_{2} (n)$,
our QAOA-based algorithm not only contributes to the ongoing discourse on qubit
efficiency but also demonstrates improved performance based on established
metrics, underscoring its potential for achieving NISQ-era supremacy in solving
real-world optimization challenges.
