FAQNAS: FLOPs-aware Hybrid Quantum Neural Architecture Search using Genetic Algorithm

TL;DR

This paper introduces FAQNAS, a FLOPs-aware neural architecture search framework for hybrid quantum neural networks that balances accuracy and computational complexity, demonstrating effective architecture discovery on multiple datasets.

Contribution

FAQNAS explicitly incorporates FLOPs into the NAS process for HQNNs, enabling the design of efficient architectures with strong performance in the NISQ era.

Findings

Quantum FLOPs dominate accuracy improvements.

Classical FLOPs remain largely fixed.

FLOPs-aware NAS achieves competitive accuracy with reduced computational cost.

Abstract

Hybrid Quantum Neural Networks (HQNNs), which combine parameterized quantum circuits with classical neural layers, are emerging as promising models in the noisy intermediate-scale quantum (NISQ) era. While quantum circuits are not naturally measured in floating point operations (FLOPs), most HQNNs (in NISQ era) are still trained on classical simulators where FLOPs directly dictate runtime and scalability. Hence, FLOPs represent a practical and viable metric to measure the computational complexity of HQNNs. In this work, we introduce FAQNAS, a FLOPs-aware neural architecture search (NAS) framework that formulates HQNN design as a multi-objective optimization problem balancing accuracy and FLOPs. Unlike traditional approaches, FAQNAS explicitly incorporates FLOPs into the optimization objective, enabling the discovery of architectures that achieve strong performance while minimizing computational cost. Experiments on five benchmark datasets (MNIST, Digits, Wine, Breast Cancer, and Iris) show that quantum FLOPs dominate accuracy improvements, while classical FLOPs remain largely fixed. Pareto-optimal solutions reveal that competitive accuracy can often be achieved with significantly reduced computational cost compared to FLOPs-agnostic baselines. Our results establish FLOPs-awareness as a practical criterion for HQNN design in the NISQ era and as a scalable principle for future HQNN systems.