QUACOD decomposes drone scheduling into quantum-solvable subproblems via coordinate descent, outperforming prior quantum methods in completion time while scaling to 5x more drones and 35x more routes.
Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets,
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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quant-ph 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
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.
Diagonal ANOs are mathematically equivalent to full ANOs modulo unitary similarity, reducing k-local observable complexity from O(4^k) to O(2^k) and lowering measurement-side classical computation while including conventional VQCs as a special case.
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.
citing papers explorer
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QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling
QUACOD decomposes drone scheduling into quantum-solvable subproblems via coordinate descent, outperforming prior quantum methods in completion time while scaling to 5x more drones and 35x more routes.
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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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Diagonal Adaptive Non-local Observables on Quantum Neural Networks
Diagonal ANOs are mathematically equivalent to full ANOs modulo unitary similarity, reducing k-local observable complexity from O(4^k) to O(2^k) and lowering measurement-side classical computation while including conventional VQCs as a special case.
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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.