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

arXiv: https://arxiv.org/abs/2511.10062 · HTML: https://mlsanity.com/paper/2511.10062/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 13 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [Quantum circuit architecture search for variational quantum algorithms](https://mlsanity.com/paper/2010.10217/) (2020)
- [A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead](https://mlsanity.com/paper/2310.10315/) (2023)
- [ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks](https://mlsanity.com/paper/2402.09146/) (2024)

## Built on by (most cited first, 5 of 5)

- [QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks](https://mlsanity.com/paper/2604.07013/) (2026)
- [Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease](https://mlsanity.com/paper/2604.13608/) (2026)
- [Hybrid Quantum-Classical Neural Architecture Search](https://mlsanity.com/paper/2605.18345/) (2026)
- [Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices](https://mlsanity.com/paper/2605.22097/) (2026)
- [MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recovery](https://mlsanity.com/paper/2607.10707/) (2026)

## Abstract (arXiv)

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.