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This research benchmarks QAOA against classical optimization methods for minimizing exam-scheduling conflicts.

July 27, 2026Open Access

Where NISQ-Era Quantum Optimization Stands Today: An Honest Benchmark of QAOA Against Classical Heuristics on a Real-World Exam-Scheduling Max-Cut Problem

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Randomized trial evaluates QAOA performance on exam-scheduling, suggesting hardware limitations hinder effectiveness.

This research benchmarks QAOA against classical optimization methods for minimizing exam-scheduling conflicts.
Evaluated QAOA alongside classical heuristics like greedy search and exact ILP on weighted Max-Cut problems.
Conducted noisy local simulations on an 18-qubit subset and live executions on a 42-qubit quantum processor.
Utilized shot-weighted expected-value scoring rather than best-observed-sample reporting.
At 18 qubits, QAOA achieved 95.4% of the proven optimum.
At 42 qubits, QAOA's expected value was 49.05%, lower than random guessing at 50.00%.
All 4,096 shots returned distinct bitstrings due to decoherence overwhelming the circuit.

ARYA et al. (2026) studied this question.

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