Quantum Neural Networks (QNNs) are a promising framework

The gap

Evidence profile

Stated in the abstract and conclusions sections of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 4 representative gaps

• Quantum Adaptive Self-Attention for Quantum Transformer Models (2026) · Quantum Science and Technology · doi

  Abstract Integrating quantum computing into deep learning architectures is a promising but poorly understood endeavor: when does a quantum layer actually help, and how much quantum is enough? We address both questions through Quantum Adaptive Self-Attention (QASA), a hybrid Transformer that replaces the value projection in a single encoder layer with a parameterized quantum circuit (PQC), while keeping all other layers classical.

  generalstated in abstractevidence 5/5
  Keywords: quantum layer abstract integrating computing deep learning architectures promising poorly understood endeavor actually help enough
• Image Classification on IBM Quantum Computers (2026) · arXiv

  Quantum machine learning on real noisy intermediate-scale quantum (NISQ) hardware has remained largely confined to binary or few-class tasks, limited by the cost of on-hardware training and the underuse of large devices at inference.

  generalstated in abstractevidence 5/5
  Keywords: quantum hardware machine learning real noisy intermediate scale nisq remained largely confined binary class tasks
• Variational quantum Kolmogorov–Arnold network (2026) · Quantum Information Processing · doi

  Future work will focus on adapting the method for use in noisy intermediate-scale quantum (NISQ) devices and exploring its robustness in practical quantum computing environments. While the results are promising, VQKAN has not yet been tested on actual quantum hardware. This ability to generalize well with limited data makes it highly suitable for quantum machine learning tasks.

  generalstated in conclusionsevidence 5/5
  Keywords: quantum future focus adapting noisy intermediate scale nisq devices exploring robustness practical computing environments promising
• Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networks (2026) · arXiv

  Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood.

  generalstated in abstractevidence 4/5
  Keywords: quantum neural networks qnns promising framework machine learning near term devices security risks remain insufficiently

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