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The study benchmarks classical, tensor-network, and quantum approaches on QUBO problems, and finds that the tested QAOA variants showed no solution-quality gain over classical solvers. The more useful takeaway for quantum readers is the methodological one: solver performance varied sharply with problem setup, and the authors could not find a reliable rule for choosing a solver from variable count alone. The article's own details are uneven, so the specific numbers are worth checking against the original paper before drawing conclusions.
Quantum QAOA fails to improve on classical QUBO solutions tested
Testing the linear-ramp quantum approximate optimization algorithm (LR-QAOA) runs offer no solution-quality improvement over the classical comparators, with a branch-and-cut solver certifying 176 instances within a 600 s cap.
A general-purpose branch-and-cut solver certified 176 out of 180 benchmark instances of quadratic unconstrained binary optimization (QUBO) problems within a 600-second time limit, demonstrating a high degree of reliability for problems up to 48 variables. However, researchers at Duale Hochschule Baden-Württemberg (DHBW) found this solver’s performance dramatically shifted when presented with scrambled optimal solutions. At 48 variables, it located only four of ten optima. In contrast, parallel tempering consistently found every optimum in every tested run within 60 seconds, highlighting the sensitivity of some solvers to initial conditions. The work compares classical, tensor-network and quantum approaches, finding that tested quantum approximate optimization algorithms offered no solution-quality improvement over classical methods.…
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