Hybrid Quantum-Classical Neural Architecture Search
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 56 references that are in the map. First posted on arXiv 18 May 2026.
Builds on
23 of 56 references.
References · 23
- 2018Barren plateaus in quantum neural network training landscapescited by 630
- 2023Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networkscited by 130
- 2020Quantum circuit architecture search for variational quantum algorithmscited by 82
- 2021A Comprehensive Survey on Hardware-Aware Neural Architecture Searchcited by 61
- 2021Quantum Architecture Search via Deep Reinforcement Learningcited by 60
- 2024Quantum Architecture Search: A Surveycited by 32
- 2023A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Aheadcited by 29
- 2023Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Modelscited by 19
- 2020NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networkscited by 16
- 2023Enhancing Quantum Support Vector Machines through Variational Kernel Trainingcited by 14
- 2023Simulation of a Variational Quantum Perceptron using Grover's Algorithmcited by 12
- 2024Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detectioncited by 10
11 more
- 2025HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Predictioncited by 10
- 2025Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generationcited by 7
- 2025A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detectioncited by 7
- 2025FAQNAS: FLOPs-aware Hybrid Quantum Neural Architecture Search using Genetic Algorithmcited by 5
- 2025Quantum Long Short-term Memory with Differentiable Architecture Searchcited by 4
- 2026QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networkscited by 4
- 2025Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogatescited by 3
- 2024Qubit-Wise Architecture Search Method for Variational Quantum Circuitscited by 2
- 2024Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learningcited by 2
- 2025RobQFL: Robust Quantum Federated Learning in Adversarial Environmentcited by 2
- 2025Neural Architecture Search for Quantum Autoencoderscited by 1
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Citing papers per year: 2026: 1.
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