Hybrid Quantum-Classical Neural Architecture Search

TL;DR

This paper explores the design and optimization of hybrid quantum-classical neural networks using neural architecture search, emphasizing hardware-aware methods for efficiency and practical deployment.

Contribution

It introduces FLOPs-aware neural architecture search for HQNNs, addressing the challenge of designing efficient, hardware-compatible quantum-classical models.

Findings

FLOPs serve as an effective proxy for computational complexity in HQNNs.

FLOPs-aware search improves the efficiency of HQNNs without sacrificing accuracy.

The study provides foundational insights into NAS for hybrid quantum-classical models.

Abstract

Hybrid quantum-classical neural networks (HQNNs) are emerging as a practical approach for quantum machine learning in the noisy intermediate-scale quantum (NISQ) era, as they combine classical learning components with parameterized quantum circuits in an end-to-end trainable framework. However, their performance and efficiency depend strongly on architectural choices such as data encoding, circuit structure, measurement design, and the coupling between classical and quantum modules. This makes manual design increasingly difficult, especially when hardware limitations and resource constraints must also be taken into account. In this paper, we study the foundations of HQNNs and neural architecture search (NAS), discuss how NAS extends to quantum and hybrid settings, and demonstrate FLOPs-aware search (where FLOPs serve as a proxy for computational complexity), as an important hardware-aware direction for building HQNNs that are not only accurate but also computationally efficient and practically deployable.