Performance Analysis of Grocer's Search Algorithm in Quantum Computing
Tallapally Mounika,
T Ramya Priya,
Thalla Umadevi
Grover’s Search Algorithm is one of the fundamental quantum algorithms for searching an unstructured
search space and is widely recognized for providing a quadratic reduction in oracle-query complexity
compared with classical exhaustive search. The algorithm achieves this improvement through quantum
superposition, an oracle that marks target states, and amplitude amplification that progressively increases
the probability of measuring a valid solution. This article presents a performance-oriented analysis of
Grover’s algorithm by examining query complexity, search-space size, iteration requirements, theoretical
success probability, multiple-solution behavior, circuit execution, and the effects of quantum noise. The
analysis compares classical linear search with Grover search for progressively larger search spaces
and presents numerical datasets suitable for direct graph generation. Theoretical results demonstrate
that the number of oracle iterations increases approximately with the square root of the search-space
size, while the probability of observing a marked state becomes high when the number of iterations is
appropriately selected. Practical performance, however, differs from the ideal theoretical model because
quantum noise, decoherence, gate errors, measurement errors, circuit depth, oracle construction, and
limited qubit connectivity reduce success probability. Experimental results reported on superconducting
quantum processors show substantial degradation between noise-free simulations and real hardware.
The study therefore distinguishes query-complexity advantage from end-to-end practical speed and
identifies oracle efficiency, hardware quality, error suppression, and optimized circuit design as critical
factors determining the usefulness of Grover’s algorithm.
10.33425/3066-1226.1316
Published: 26 Sep, 2026