  We suggest employing graph sparsification as a pre-processing step for maxcut
programs using the QUBO solver. Quantum(-inspired) algorithms are recognized
for their potential efficiency in handling quadratic unconstrained binary
optimization (QUBO). Given that maxcut is an NP-hard problem and can be readily
expressed using QUBO, it stands out as an exemplary case to demonstrate the
effectiveness of quantum(-inspired) QUBO approaches. Here, the non-zero count
in the QUBO matrix corresponds to the graph's edge count. Given that many
quantum(-inspired) solvers operate through cloud services, transmitting data
for dense graphs can be costly. By introducing the graph sparsification method,
we aim to mitigate these communication costs. Experimental results on
classical, quantum-inspired, and quantum solvers indicate that this approach
substantially reduces communication overheads and yields an objective value
close to the optimal solution.
