Quantum machine learning on real noisy intermediate-scale
Research gap analysis derived from 3 physics papers in our local library.
The gap
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.
Evidence profile
Sourced from the abstract and conclusions of the source papers, classified as general, spanning 3 journals.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 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.
generalabstractevidence 5/5Keywords: 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.
generalabstractevidence 5/5Keywords: 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 5/5Keywords: quantum future focus adapting noisy intermediate scale nisq devices exploring robustness practical computing environments promising
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