Hybrid Quantum-Classical Neural Architecture Search
For researchers and practitioners in quantum machine learning, this work provides a method to automate the design of HQNNs, which is crucial for practical deployment in the NISQ era.
This paper addresses the challenge of manually designing hybrid quantum-classical neural networks (HQNNs) by introducing a neural architecture search (NAS) approach that optimizes architectural choices like data encoding and circuit structure. The authors demonstrate FLOPs-aware search to build HQNNs that are both accurate and computationally efficient.
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.