Grover’s quantum search algorithm provides a fundamental quadratic speedup for unstructured datasets, reducing query complexity from $𝒪(N)$ to $𝒪(\sqrt{N})$. However, the algorithm’s reliance on precise iteration counts leads to the "soufflé problem," where over-rotation results in a sharp decline in success probability. This limitation is particularly restrictive when the number of solution states, $M$, is unknown. In this work, we present an ancilla-mediated fixed-point quantum search algorithm that achieves robust convergence by mapping the solution amplitude to a dedicated ancilla qubit. Unlike existing phase-matching fixed-point methods, our approach utilizes Grover’s real-plane reflections, thereby maintaining the intuitive geometric architecture of the original algorithm. We demonstrate that this method achieves a success probability of at least $92.6\%$ with a query complexity of approximately $𝒪(\sqrt{N/M})$, effectively bridging the gap between standard amplitude amplification and robust fixed-point convergence.