In this study, we explore the potential of Quantum Fourier Transform (QFT) for analyzing low frequency signals focusing on noise resilience using gravitational wave detection. Traditional Fourier Transform techniques often struggle to effectively analyze low-frequency components in finite-duration signals, particularly in the presence of noise. QFT provides a unique approach to represent and process frequency information with fine granularity, utilizing quantum superposition and interference even when the signal’s fundamental frequency resolution is constrained by its duration. To enhance frequency resolution, we use gravitational wave data from the LIGO database sampled at 16 kHz, analyzing a 32-s signal duration. Amplitude and Phase based quantum state encoding was employed to map the signal onto quantum states, and QFT circuits were implemented for 3 to 4 qubits, through repeated quantum measurements by extracting probability distributions. Compared to Classical Fourier analysis, the results demonstrate the QFT’s heightened sensitivity to low-frequency components and its robustness against noise interference. Our findings reveal that effective noise suppression in QFT scales inversely with the number of qubits, offering a computational advantage in isolating weak low-frequency signals. This establishes QFT as a promising tool for signal processing tasks requiring high precision in low-frequency regimes, with significant implications for gravitational wave detection and related fields.