Noise-Aware Quantum Architecture Search Based on NSGA-II Algorithm

TL;DR

This paper introduces a noise-aware quantum architecture search framework using an enhanced NSGA-II algorithm, optimizing quantum circuit designs for robustness and efficiency under noisy conditions, validated on classification tasks.

Contribution

It presents a novel noise-aware QAS framework incorporating a hybrid evaluation strategy and an improved NSGA-II algorithm for better architecture optimization.

Findings

Achieves superior classification performance under noise.

Identifies architectures with better resource efficiency.

Demonstrates robustness of designs in noisy environments.

Abstract

Quantum architecture search (QAS) has emerged to automate the design of high-performance quantum circuits under specific tasks and hardware constraints. We propose a noise-aware quantum architecture search (NA-QAS) framework based on variational quantum circuit design. By incorporating a noise model into the training of parameterized quantum circuits (PQCs) , the proposed framework identifies the noise-robust architectures. We introduce a hybrid Hamiltonian $\varepsilon$ -greedy strategy to optimize evaluation costs and circumvent local optima. Furthermore, an enhanced variable-depth NSGA-II algorithm is employed to navigate the vast search space, enabling an automated trade-off between architectural expressibility and quantum hardware overhead. The effectiveness of the framework is validated through binary classification and iris multi-classification tasks under a noisy condition. Compared to existing approaches, our framework can search for quantum architectures with superior performance and greater resource efficiency under a noisy condition.