Fluid dynamics simulations, whilst computationally costly, are commercially desirable. Our current methods to study turbulent regimes rely on grid discretization which demands a high number of grid points. This quickly becomes unfeasible for large simulations. Therefore, new computational paradigms are necessary. To that end, Quantum Computing has been suggested as a promising candidate given the exponential advantage in grid points by using qubits. In this paradigm, different approaches have been developed, including linearization [1] and spectral decomposition [2]. A variational quantum algorithm was developed by Lubasch et all [3] that has potential to be run on current pre fault tolerant hardware and solve nonlinear problems in fluid dynamics. Recent studies have shown how to deploy this and optimize this algorithm in trapped-ion computers [4]. In this work, we study the application of this algorithm in Superconducting Quantum processors, which bring unique challenges in terms of topology of the circuit and ansatz structure, and opportunities in terms of shots, gate speeds and error mitigation. Furthermore, we study how error mitigation methods such as Measurement Error Mitigation [5], Dynamical Decoupling [6], Zero-Noise Extrapolation [7] and Echo Verification [8] can be used to further enhance the algorithm. We present our results regarding the Inviscid and Viscid Burgers Equation and show howthese can be solved in current superconducting hardware.