A distributed bilevel algorithm optimizes emergent macroscopic behavior in multi-agent systems by combining local exponential-family state estimation with hypergradient microscopic updates and proves convergence via timescale separation.
Methods of conjugate gradients for solving linear systems,
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QAOA-based QuSO achieves end-to-end speedup over classical baselines for power grid unit commitment with up to 14 qubits using 16 layers in high-load scenarios via efficient classical pre-computation.
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A Distributed Bilevel Framework for the Macroscopic Optimization of Multi-Agent Systems
A distributed bilevel algorithm optimizes emergent macroscopic behavior in multi-agent systems by combining local exponential-family state estimation with hypergradient microscopic updates and proves convergence via timescale separation.
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End-to-End Speedup for Quantum Simulation-Based Optimization in Power Grid Management
QAOA-based QuSO achieves end-to-end speedup over classical baselines for power grid unit commitment with up to 14 qubits using 16 layers in high-load scenarios via efficient classical pre-computation.