Sparse autoencoders applied to Neural Quantum States extract unsupervised features correlating with and causally steering physical observables such as order parameters while preserving variational energy.
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PSR-NQS makes recurrent neural quantum states scalable for variational Monte Carlo by using parallel scan recurrence, reaching accurate results on 52x52 two-dimensional lattices.
The paper proposes variational decision diagrams (VDDs) for quantum state representation in QML and reports successful training without barren plateaus on transverse-field Ising and Heisenberg Hamiltonians.
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Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoders
Sparse autoencoders applied to Neural Quantum States extract unsupervised features correlating with and causally steering physical observables such as order parameters while preserving variational energy.
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Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo
PSR-NQS makes recurrent neural quantum states scalable for variational Monte Carlo by using parallel scan recurrence, reaching accurate results on 52x52 two-dimensional lattices.
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Variational decision diagrams for quantum-inspired machine learning applications
The paper proposes variational decision diagrams (VDDs) for quantum state representation in QML and reports successful training without barren plateaus on transverse-field Ising and Heisenberg Hamiltonians.