analiza aiqbench

Researchers have integrated deterministic simplification rules into a reinforcement learning framework to significantly reduce gate counts in twenty-qubit quantum circuits, achieving up to a 44.9% reduction compared to standard methods. This approach prevents agents from redundantly relearning basic reductions, thereby focusing computational resources on non-trivial optimizations that enhance efficiency for noisy intermediate-scale quantum (NISQ) devices. While the study demonstrates superior performance over existing transpilers like Qiskit's on current hardware scales, further evaluation is needed to confirm scalability toward larger systems.