Present study investigates the integration of hybrid quantum-classical computing into the inverse aerodynamic design problem, focusing on airfoil optimization, by exploring gate-based quantum architectures. Traditional Computational Fluid Dynamics (CFD) algorithms stands to benefit from the potential speed-up of quantum architectures. The Douglas-Neumann panel method has been used to model incompressible, inviscid flow, framing the optimization as a search for geometric parameters that minimize the vortex distribution for a symmetric NACA 0012 airfoil, given a target vortex distribution. The gate-based strategy has been investigated, proposing a methodology that leverages a Variational Quantum Algorithm (VQA) framework, integrating classical optimizers like Adam and Genetic Algorithms (GA) with two quantum circuits based on the Swap Test and Inverse Test to state encoding and to estimate the minimization of the overlapped vortex distributions as quantum loss functions to measure state fidelity directly on the quantum register. This methodology exploits quantum interference to extract information about state similarity, in order to provide a measure that can be minimized through classical optimization. Furthermore, a non-variational approach using Grover search algorithm has been implemented. It operates as a purely quantum search method, directly amplifying the probability of finding optimal parameter configurations through quantum amplitude amplification. Numerical experiments, executed via Qiskit, compare ideal statevector simulations with real-world noisy hardware performance on the IBM Sherbrooke processor. Results demonstrate that while hybrid workflows accurately replicate classical benchmarks in noise-free environments, hardware noise introduces significant bottlenecks. Comparative analysis shows that Genetic Algorithms exhibit superior robustness to noise in low-dimensional spaces, whereas gradient-based Adam optimization offers better scalability as the parameter count increases. The high circuit depth and accumulation of two-qubit gate errors render Grover’s algorithm less effective on current hardware compared to variational methods. Conceptual feasibility of quantum-assisted aerodynamic design have been demonstrated, though NISQ era hardware noise limitations.