For diagonal Hamiltonians like MaxCut, hardware-efficient ansatze drive entanglement down during training and are outperformed by separable circuits in a monotonic relationship, while QAOA's problem-derived entanglement remains competitive.
Improving variational quantum optimization using cvar,
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D-QEO framework uses quantum topographical preconditioning on separable functions via small parallel subcircuits to generate seeds that accelerate classical global optimization and avoid exponential failure rates.
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Detrimental Agnostic Entanglement: The Case Against Hardware-Efficient Ans\"atze for Combinatorial Optimization
For diagonal Hamiltonians like MaxCut, hardware-efficient ansatze drive entanglement down during training and are outperformed by separable circuits in a monotonic relationship, while QAOA's problem-derived entanglement remains competitive.
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Distributed Quantum-Enhanced Optimization: A Topographical Preconditioning Approach for High-Dimensional Search
D-QEO framework uses quantum topographical preconditioning on separable functions via small parallel subcircuits to generate seeds that accelerate classical global optimization and avoid exponential failure rates.