The problem of barren plateaus in quantum machine learning

The gap

Evidence profile

Sourced from the stated challenges and conclusions and future work of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Compositional Quantum Heuristics for Max-Clique Detection (2026) · Quantum Science and Technology · doi

  The problem of barren plateaus in quantum machine learning. The need for efficient training and evaluation of quantum circuits on a quantum device without being classically simulable. The challenge of isolating a setting where quantum circuits can provide a computational or learning theoretic advantage.

  generalstated challenges
  Keywords: problem barren plateaus quantum machine learning need efficient
• 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.

  generalconclusionsevidence 5/5
  Keywords: quantum future focus adapting noisy intermediate scale nisq devices exploring robustness practical computing environments promising
• A quantum bagging algorithm with unsupervised base learners for label corrupted datasets (2026) · Journal of Computational Science · doi

  Building on the encouraging results under noisy conditions, future work can explore theoretical analysis of variance reduction in quantum unsupervised bagging and extend 22 the framework to more complex or high-dimensional datasets. Extending the method to semi-supervised or active learning scenarios and adapting it for NISQ-era hardware implementations are also compelling directions for expanding the practical impact of quantum bagging. Acknowledgements. The first author Neeshu Rathi is thankful to the University Grants Commission(UGC), New Delhi, India, for providing finantial support in the form of a senior research fellowship with UGC Ref. No.: 1136/(CSIR-UGC NET JUNE 2019).

  generalfuture workevidence 5/5
  Keywords: quantum bagging building encouraging noisy conditions future explore theoretical variance reduction unsupervised extend framework complex

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