TL;DR
These results identify the conditions under which a compact variational circuit can be integrated into a classical pipeline and characterize the noise resilience of the resulting model, indicating that input robustness is governed by the shared classical encoder.
The noise of quantum-enhanced learning there is a potential of quantum-enhanced learning with noisy intermediate-scale quantum (NISQ) devices, which is limited by hardware noise and finding useful circuit architectures. In this paper, a Noise-Aware Adaptive Variational Quantum Neural Network (NAVQNN) structure is propo...
The practical value of quantum machine learning for network anomaly detection remains unclear under controlled and comparable benchmarking conditions. This study evaluates a trash-qubit-based Quantum Autoencoder (QAE) against seven classical anomaly-detection methods on UNSW-NB15 using shared data partitions, preproces...
This paper repurposes uniformly controlled rotations from static state preparation and static data encoding into a state-encoding interface for quantum neural networks, elevating the process of integrating classical states into quantum neural networks to an independent method-ological layer and achieves a more stable p...
Results show that FL enables high-performing, privacy-preserving quantum-classical collaboration without centralizing raw data, and achieves this with substantially fewer trainable parameters than the classical neural networks and random forest alternatives.
General operator learning for parametric partial differential equations (PDEs) is a fundamental challenge at the intersection of artificial intelligence and physics-based modeling. Physics-informed Deep Operator Networks (PI-DeepONets) incorporate governing equations into learning, but face optimization difficulties in...
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