Overfitting in traditional Quantum Neural Networks (QNNs)
Research gap analysis derived from 3 physics papers in our local library.
The gap
Overfitting in traditional Quantum Neural Networks (QNNs). The complexity of the parameter space in quantum neural networks, which often leads to unstable and suboptimal performance. The need for efficient computation using matrix operation
Evidence profile
Sourced from the abstract and stated challenges of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 3 journals. Those papers have been cited 4 times in total.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- Exploring the experimental limit of deep quantum signal processing using a trapped-ion simulator (2025) · Physical Review Applied · cited 4× · doi
This work addresses a key gap in understanding the scalability and limitations of QSP-based algorithms on quantum hardware, providing valuable insights for developing quantum algorithms as well as practically realizing quantum singular value transformation and data reuploading quantum machine learning models.
generalabstractKeywords: quantum algorithms addresses understanding scalability limitations based hardware providing valuable insights developing well practically realizing - Quantum Artificial Intelligence for Software Engineering: The Road Ahead (2026) · ACM Transactions on Software Engineering and Methodology · doi
The increasing complexity of software systems. The lack of large, balanced, and diverse datasets for training classical machine learning models. The need to address challenges in applying QAI to software engineering, such as the development of quantum computing hardware and software.
generalstated challengesevidence 5/5Keywords: increasing complexity software systems lack large balanced diverse - Variational quantum Kolmogorov–Arnold network (2026) · Quantum Information Processing · doi
Overfitting in traditional Quantum Neural Networks (QNNs). The complexity of the parameter space in quantum neural networks, which often leads to unstable and suboptimal performance. The need for efficient computation using matrix operations in quantum circuits.
generalstated challengesevidence 5/5Keywords: overfitting traditional quantum neural networks qnns complexity parameter
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