Noise-Aware Evaluation and Adaptive Optimization Level Selection for Quantum Circuits in NISQ Systems

Abstract

Quantum circuit optimization plays a critical role in improving the performance of algorithms executed on noisy intermediate-scale quantum (NISQ) devices. While modern quantum compilers provide multiple optimization levels, selecting an appropriate optimization strategy remains a non-trivial task due to the complex interplay between circuit structure and hardware noise. In this work, we present a comprehensive noise-aware evaluation of quantum circuit optimization strategies using a diverse set of 33 circuits, including Quantum Fourier Transform (QFT), Grover's search, Variational Quantum Eigensolver (VQE), and randomly generated circuits. We analyze the impact of different transpiler optimization levels on circuit depth, entanglement (CNOT count), and output fidelity under a realistic depolarizing noise model. Our experimental results reveal that no single optimization level consistently yields the best performance across all circuit types. While aggressive optimization is beneficial for structured circuits such as QFT and VQE, moderate optimization levels often provide a better trade-off between circuit complexity and fidelity for unstructured or randomly generated circuits. These findings highlight that optimization-level selection is inherently dependent on circuit characteristics and cannot be effectively addressed using static or heuristic approaches alone. To address this challenge, we further propose a lightweight learning-based model that predicts the optimal optimization level based on circuit features. The model achieves an accuracy of 93.9% across the evaluated dataset, demonstrating that simple machine learning techniques can effectively approximate optimal compiler decisions without requiring exhaustive evaluation. Overall, this work provides practical insights into optimization behavior in NISQ systems and introduces a scalable approach for adaptive optimization selection, contributing toward more efficient quantum software development.