Recent advances in quantum machine learning (QML) offer

The gap

Evidence profile

Sourced from the conclusions and stated research gap and future-work section and synthesized and abstract of the source papers, classified as general, spanning 7 journals. Those papers have been cited 6 times in total.

Supporting evidence — 8 representative gaps

• 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
• Drug Discovery Acceleration Using Quantum Simulations (2026) · International Journal of Emerging Research in Science, Engineering, and Management · doi

  The traditional drug discovery process is time-consuming, expensive, and lacks efficiency. The existing methodology for drug discovery is characterized by a fragmented reliance on traditional experimental approaches. Quantum computing and advanced computational techniques offer a new approach to address these challenges.

  generalstated research gapevidence 5/5
  Keywords: traditional drug discovery process time-consuming expensive lacks efficiency
• Noise-induced shallow circuits and the absence of barren plateaus (2026) · Nature Physics · cited 6× · doi

  The paper suggests future research directions, such as exploring the implications of the results for variational quantum machine learning proposals. The paper suggests future research directions, such as studying the impact of uncorrected local noise on logical quantum circuits in different scenarios.

  generalfuture-work sectionevidence 5/5
  Keywords: paper suggests future research directions exploring implications results
• Quantum Computing : Fundamental Principles, Quantum Algorithms, and Emerging Applications (2026) · International Journal of Advanced Research in Science Communication and Technology · doi

  None of these studies evaluate quantum machine learning methods for molecular property prediction (QSAR/ADMET) on public drug-discovery datasets with explicit comparison to classical baselines and noise-robustness analysis on NISQ hardware. Existing work discusses quantum algorithms in principle but does not benchmark variational quantum circuits or quantum kernels against standard molecular fingerprints on real molecular datasets with measured generalization and uncertainty quantification.

  generalsynthesizedevidence 5/5
  Keywords: none studies evaluate quantum machine learning methods molecular
• Quantum Computing : Fundamental Principles, Quantum Algorithms, and Emerging Applications (2026) · International Journal of Advanced Research in Science Communication and Technology · doi

  No paper in this collection reports a reproducible, open-source implementation of a quantum machine learning model for any drug-discovery or genomics task with publicly available code, trained model, and benchmark results—limiting practical adoption and verification for the researcher's 2-day implementation timeline.

  generalsynthesizedevidence 5/5
  Keywords: paper collection reports reproducible open-source implementation quantum machine
• Quantum Kernel Regression and Variational Quantum Regression for the CCS Prediction of Dissolved Organic Matter: A Comparative Benchmarking Study (2026) · Journal of the American Society for Mass Spectrometry · doi

  Abstract Recent advances in quantum machine learning (QML) offer new opportunities for chemical property prediction, yet their practical performance relative to established classical models remains poorly characterized on real-world datasets.

  generalabstractevidence 5/5
  Keywords: abstract recent advances quantum machine learning offer opportunities chemical property prediction practical performance relative established
• Advantage of quantum machine learning from general computational advantages (2026) · npj Quantum Information · doi

  The lack of a broader family of supervised learning tasks that can be used to demonstrate the advantage of quantum machine learning based on general quantum computational advantages. The limited focus of prior work on specific quantum algorithms, such as Shor’s algorithms. The need for a proof of the hardness of achieving this learning task for any possible polynomial-time classical learning method.

  generalstated research gapevidence 5/5
  Keywords: lack broader family supervised learning tasks used demonstrate
• Hybrid Quantum-Classical Neural Architecture Search (2026) · arXiv

  Further research is needed to explore the application of the proposed methodology to various quantum machine learning tasks. The incorporation of additional objectives, such as energy consumption, could be explored. The development of more efficient algorithms for searching the quantum circuit configuration space is an area for future research.

  generalfuture-work sectionevidence 4/5
  Keywords: further research needed explore application proposed methodology various

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