BUP-TR completes underdetermined quadratic models via Bayesian projection in the prior precision norm, yielding fully linear hard-MAP models under stated conditions and attaining global first-order convergence with O(ε^{-2}) complexity.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
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.
A new optimization-based calibration method allows accurate spatially varying power-law attenuation modeling in ultrasound wave simulations with mean errors below 3%.
citing papers explorer
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BUP-TR: Bayesian Underdetermined Projection Trust-Region Methods for Derivative-Free Optimization
BUP-TR completes underdetermined quadratic models via Bayesian projection in the prior precision norm, yielding fully linear hard-MAP models under stated conditions and attaining global first-order convergence with O(ε^{-2}) complexity.
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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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Spatially heterogeneous power-law attenuation with multiple relaxation mechanisms for ultrasound modeling
A new optimization-based calibration method allows accurate spatially varying power-law attenuation modeling in ultrasound wave simulations with mean errors below 3%.