  The Grover search algorithm is a pivotal advancement in quantum computing,
promising a remarkable speedup over classical algorithms in searching
unstructured large databases. Here, we report results for the implementation
and characterization of a three-qubit Grover search algorithm using the
state-of-the-art scalable quantum computing technology of superconducting
quantum architectures. To delve into the algorithm's scalability and
performance metrics, our investigation spans the execution of the algorithm
across all eight conceivable single-result oracles, alongside nine two-result
oracles, employing IBM Quantum's 127-qubit quantum computers. Moreover, we
conduct five quantum state tomography experiments to precisely gauge the
behavior and efficiency of our implemented algorithm under diverse conditions;
ranging from noisy, noise-free environments to the complexities of real-world
quantum hardware. By connecting theoretical concepts with real-world
experiments, this study not only shed light on the potential of NISQ (Noisy
Intermediate-Scale Quantum) computers in facilitating large-scale database
searches but also offer valuable insights into the practical application of the
Grover search algorithm in real-world quantum computing applications.
