Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment

arXiv:2609.28491 · quant-ph, cs.AI, cs.LG · Submitted 2026-08-30 · Read on arXiv

quant-ph, cs.AI, cs.LG

Submitted: 2026-08-30

Updated: 2026-08-30

Code: https://github.com/Dilli822/SimAM-HVQC

Terminology

• Accuracy — 6× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Latent Representation — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Variational Quantum Circuit — 3× in this paper
• Adaptive Weighting — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Entanglement — 1× in this paper · explained in 3 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• Variational Quantum Neural Networks (VQNNS) in Image Classification
• Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
• Continuous Variable Quantum MNIST Classifiers
• A Comparative Analysis of Hybrid-Quantum Classical Neural Networks
• Hybrid Quantum-Classical Learning for Multiclass Image Classification

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