Understanding Noise-Adaptive Transpilation Techniques Using the SupermarQ Benchmark

Quantum computing promises to solve complex problems beyond the capabilities of classical computers. However, the practical implementation of quantum algorithms on Noisy Intermediate-Scale Quantum (NISQ) devices is significantly challenged by various noise sources that degrade computational fidelity. Transpilation—the process of converting high-level quantum circuits into optimized, hardware-specific instructions—plays a crucial role in mitigating these noise effects. In this paper, we present a comprehensive study on enhancing noise resilience in quantum algorithms through advanced transpilation techniques, evaluated using the SupermarQ benchmarking suite. By systematically analyzing the impact of different optimization levels in Qiskit’s transpiler on a diverse set of quantum algorithms, we demonstrate how specific transpilation strategies influence key performance metrics related to noise resilience. Our results show that higher optimization levels can improve metrics such as Liveness (LV) and Parallelism (PL), leading to more reliable quantum computations on NISQ hardware. This work contributes to the development of noise-adaptive transpilation methods that incorporate noise characteristics into the optimization process, ultimately enhancing the practical viability of quantum algorithms.