CRiSP uses neural-guided MCTS and curriculum learning to insert Clifford prefixes before parameterized rotations in VQAs, yielding mean 3.17x and max 45x gains in energy accuracy on 22-qubit QAOA benchmarks versus prior Clifford initializers.
Implementing the Nelder-Mead simplex algorithm with adaptive parameters , url =
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A new three-point inverse solution using the α-β model reconstructs meteoroid masses and bulk densities from limited fireball observations, achieving 88% convergence on the EN catalog and producing a continuous density range of 300-4000 kg m^{-3} instead of discrete PE categories.
Neural mean-field games integrate mean-field game theory with neural SDEs to learn strategic interactions from data in a model-free way, demonstrated on games and viral dynamics.
The ECA-NM hybrid optimization produces chemical-diffusive models that reproduce major flame and detonation properties from detailed chemistry while cutting global error by four orders of magnitude and computational cost by two orders relative to genetic algorithms.
citing papers explorer
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Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
CRiSP uses neural-guided MCTS and curriculum learning to insert Clifford prefixes before parameterized rotations in VQAs, yielding mean 3.17x and max 45x gains in energy accuracy on 22-qubit QAOA benchmarks versus prior Clifford initializers.
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Consistency between dynamical modeling and photometrically derived masses of fireballs
A new three-point inverse solution using the α-β model reconstructs meteoroid masses and bulk densities from limited fireball observations, achieving 88% convergence on the EN catalog and producing a continuous density range of 300-4000 kg m^{-3} instead of discrete PE categories.
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Neural Mean-Field Games: Extending Mean-Field Game Theory with Neural Stochastic Differential Equations
Neural mean-field games integrate mean-field game theory with neural SDEs to learn strategic interactions from data in a model-free way, demonstrated on games and viral dynamics.
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An efficient method based on the evolutionary center algorithm for optimizing chemical-diffusive models for flame acceleration and DDT
The ECA-NM hybrid optimization produces chemical-diffusive models that reproduce major flame and detonation properties from detailed chemistry while cutting global error by four orders of magnitude and computational cost by two orders relative to genetic algorithms.