Quantum machine learning on real noisy intermediate-scale

The gap

Evidence profile

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

Supporting evidence — 8 representative gaps

• 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.

  generalsynthesized
  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.

  generalsynthesized
  Keywords: paper collection reports reproducible open-source implementation quantum machine
• 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.

  generalabstractevidence 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.

  generalconclusionsevidence 5/5
  Keywords: quantum future focus adapting noisy intermediate scale nisq devices exploring robustness practical computing environments promising
• ADMET Property Prediction with Quantum-Inspired Preprocessing (2026) · bioRxiv · doi

  The lack of efficient and accurate methods for ADMET property prediction. The need for compact and informative representations of molecular descriptors. The potential of quantum-inspired methods for improving ADMET property prediction.

  generalstated research gapevidence 5/5
  Keywords: lack efficient accurate methods admet property prediction need
• ADMET Property Prediction with Quantum-Inspired Preprocessing (2026) · bioRxiv · doi

  Evaluating the approach on other ADMET endpoints and datasets. Investigating the effect of the thresholds on circuit size and model performance. Exploring the application of quantum-inspired methods to other machine learning tasks in drug discovery.

  generalfuture-work sectionevidence 5/5
  Keywords: evaluating approach other admet endpoints datasets investigating effect
• 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

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