Investigation of Hardware Architecture Effects on Quantum Algorithm Performance: A Comparative Hardware Study

TL;DR

This study systematically benchmarks five quantum algorithms across different hardware platforms, revealing significant performance variations influenced by hardware architecture, which underscores the importance of hardware-aware algorithm selection in the NISQ era.

Contribution

It provides a comprehensive comparison of quantum algorithm performance on diverse hardware, highlighting architecture-dependent effects and offering practical benchmarking insights.

Findings

GHZ state fidelity above 0.8 on trapped-ion hardware

Superconducting platforms' fidelity drops below 0.15

Performance strongly depends on hardware topology and noise

Abstract

Cloud-accessible quantum processors enable direct execution of quantum algorithms on heterogeneous hardware platforms. Unlike classical systems, however, identical quantum circuits may exhibit substantially different behavior across devices due to architectural variations in qubit connectivity, gate fidelity, and coherence times. In this work, we systematically benchmark five representative quantum algorithms - Bell state preparation, GHZ state generation, Quantum Fourier Transform (QFT), Grover's Search, and the Quantum Approximate Optimization Algorithm (QAOA) - across trapped-ion, superconducting, and simulator backends using Amazon Braket. Performance metrics including fidelity, CHSH violation, success probability, circuit depth, and gate counts are evaluated. Our results demonstrate a strong dependence of algorithmic performance on hardware topology and noise characteristics. For example, 10-qubit GHZ states achieved fidelities above 0.8 on trapped-ion hardware, while superconducting platforms dropped below 0.15 due to routing overhead and accumulated two-qubit gate errors. These findings highlight the importance of hardware-aware algorithm selection and provide practical guidance for benchmarking in the NISQ era.