Structural f-divergence yields tight trade-off inequalities bounding gradient magnitudes and cost moments in parameterized quantum circuits, with equality for a minimal one-qubit ansatz.
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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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Structural $f$-divergence: Tight universal bounds for cost function moments and gradients in parameterized quantum circuits
Structural f-divergence yields tight trade-off inequalities bounding gradient magnitudes and cost moments in parameterized quantum circuits, with equality for a minimal one-qubit ansatz.
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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.