Hybrid Quantum Neural Networks: Theory, Implementations, and Applications

2026-08-02

AI Summary

Evaluating the practical utility of hybrid quantum neural networks remains challenging due to their architectural diversity, lack of standardized benchmarks, and varying hardware assumptions. This work presents the first systematic synthesis of the field’s theoretical foundations, prevailing architectures, training strategies, and hardware-aware implementation approaches, integrating perspectives from both classical and quantum machine learning to map the current research landscape and future trajectories. Findings indicate that such models demonstrate promising potential in specific tasks by achieving competitive performance with substantially fewer trainable parameters; however, they have yet to establish a clear advantage on large-scale problems. By offering a coherent framework and identifying key research directions, this study underscores the innovative value of hybrid quantum neural networks in parameter-efficient learning and specialized applications.

Abstract

Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, and hybrid quantum neural networks, which combine classical neural-network components with quantum information processing units, have emerged as a practical framework for near-term quantum technologies. However, the rapid development of the field across diverse architectures, benchmarks and hardware assumptions makes it difficult to assess the utility of various proposals, identify where genuine advantages may arise, and determine how practitioners can use these models. While recent benchmarks caution that such gains have not yet been demonstrated at scale, theoretical work has identified tasks on which quantum models hold provable advantages, and hybrid approaches have delivered promising results on practical problems using deliberately compact quantum components and substantially fewer trainable parameters. Here, we review hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities. We summarize their main theoretical and methodological foundations, survey some of the most promising architectures developed so far, and examine their implementation challenges and reported performance. By consolidating these perspectives, this review provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.

Problem

• Hybrid Quantum Neural Networks
• Quantum Machine Learning
• Benchmarking
• Model Utility
• Implementation Challenges

Innovation

• Hybrid Quantum Neural Networks
• Quantum Machine Learning
• Compact Quantum Components
• Trainable Parameters Reduction
• Near-term Quantum Technologies