  Distributed quantum computation has garnered immense attention in the noisy
intermediate-scale quantum (NISQ) era, where each computational node
necessitates fewer qubits and quantum gates. In this paper, we focus on a
generalized search problem involving multiple targets within an unordered
database and propose a Distributed Exact Generalized Grover's Algorithm (DEGGA)
to address this challenge by decomposing it into arbitrary $t$ components,
where $2 \leq t \leq n$. Specifically, (1) our algorithm ensures accuracy, with
a theoretical probability of identifying the target states at $100\%$; (2) if
the number of targets is fixed, the pivotal factor influencing the circuit
depth of DEGGA is the partitioning strategy, rather than the magnitude of $n$;
(3) our method requires a total of $n$ qubits, eliminating the need for
auxiliary qubits; (4) we elucidate the resolutions (two-node and three-node) of
a particular generalized search issue incorporating two goal strings (000000
and 111111) by applying DEGGA. The feasibility and effectiveness of our
suggested approach is further demonstrated by executing the quantum circuits on
MindSpore Quantum (a quantum simulation software). Eventually, through the
decomposition of multi-qubit gates, DEGGA diminishes the utilization of quantum
gates by $90.7\%$ and decreases the circuit depth by $91.3\%$ in comparison to
the modified Grover's algorithm by Long. It is increasingly evident that
distributed quantum algorithms offer augmented practicality.
