Rethinking Quantum Continual Learning with Quantum Fisher Information
Quantum continual learning aims to train quantum models on sequential tasks without losing previously learned knowledge. However, variational quantum classifiers (VQCs) are prone to catastrophic forgetting under nonstationary task distributions. We propose quantum elastic weight consolidation (QEWC), a quantum Fisher information (QFI)-informed regularization method for mitigating forgetting. Unlike conventional elastic weight consolidation based on classical Fisher information (CFI), which measures parameter importance through measurement-dependent output statistics, QEWC uses QFI to quantify
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- PossiblePossibly related (embedding) · 46%Live Continual Learning in Machine Learning [D] →
- PossiblePossibly related (embedding) · 45%Quantum Machine Learning Assesses Post-Quantum Protocol Resilience - Quantum Zeitgeist →
- FuzzyOverlapping authors or contributors · 62%Zeyi-Lin/HivisionIDPhotos →
“Shared author/contributor keys: lin”
- FuzzyOverlapping authors or contributors · 62%hiyouga/LlamaFactory →
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- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Rethinking Quantum Continual Learning with Quantum Fisher In” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Yu-Chao Hsu →
“Rethinking Quantum Continual Learning with Quantum Fisher Information”
- LinkedLinked via arxiv author · 85%Yu-Cheng Lin →
“Rethinking Quantum Continual Learning with Quantum Fisher Information”
- LinkedLinked via arxiv author · 85%Tai-Yue Li →
“Rethinking Quantum Continual Learning with Quantum Fisher Information”