arXiv:2606.09964v1 Announce Type: cross Abstract: The NISQ era places stringent constraints on quantum computation, where noise and decoherence fundamentally limit performance. In classical deep learning, model robustness and resilience to perturbations are well studied: deep neural networks (DNNs) maintain high performance despite pruning, noise injection, and structural perturbations due to inherent redundancy in their representations. A central challenge in quantum machine learning is to transfer this notion of robustness to quantum neural networks (QNNs) under realistic NISQ noise. While c
The proliferation of noisy intermediate-scale quantum (NISQ) devices necessitates robust quantum machine learning (QML) models capable of operating despite inherent hardware limitations, pushing research into practical QNN robustness.
Achieving robustness in quantum neural networks (QNNs) is critical for their practical deployment and for realizing the potential of quantum machine learning in real-world applications.
This research provides a framework for assessing and potentially improving the resilience of QNNs to noise, which is a major hurdle for current quantum computing efforts.
- · Quantum computing hardware developers
- · Quantum machine learning researchers
- · Industries seeking quantum advantage
- · Classical optimization techniques (in the long term)
- · Companies unable to develop robust QML models
Improved understanding and methodology for building resilient quantum machine learning models on current noisy quantum hardware.
Accelerated development and adoption of quantum machine learning applications as reliability and performance in noisy environments increase.
Widened accessibility and impact of quantum computing paradigms, potentially driving new technological and scientific breakthroughs.
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