QEL is the first quantum end-to-end learning framework for contextual combinatorial optimization using QAOA with a context re-uploading phase-separator, achieving competitive performance with fewer parameters.
A rewriting system for convex optimization problems.Journal of Control and Decision, 5(1):42–60, 2018
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Derives closed-form one-shot RDC/DRC tradeoffs for Bernoulli sources, LP-based DC regions, and bounds on asymptotic rate penalties for universal encoders supporting families of operating points.
The authors develop a flexible differentiable coil proxy based on QUADCOIL for quasi-single-stage stellarator optimization and demonstrate it with improved permanent-magnet and coil solutions for the MUSE and ARIES-CS devices.
FedUCA formalizes the server as an optimizer that uses utility-constrained stochastic aggregation to maximize client retention and global performance in heterogeneous federated learning.
citing papers explorer
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Quantum End-to-End Learning for Contextual Combinatorial Optimization
QEL is the first quantum end-to-end learning framework for contextual combinatorial optimization using QAOA with a context re-uploading phase-separator, achieving competitive performance with fewer parameters.
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Rate-Distortion-Classification Representation Theory for Bernoulli Sources
Derives closed-form one-shot RDC/DRC tradeoffs for Bernoulli sources, LP-based DC regions, and bounds on asymptotic rate penalties for universal encoders supporting families of operating points.
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A flexible and differentiable coil proxy for stellarator equilibrium optimization
The authors develop a flexible differentiable coil proxy based on QUADCOIL for quasi-single-stage stellarator optimization and demonstrate it with improved permanent-magnet and coil solutions for the MUSE and ARIES-CS devices.
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Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation
FedUCA formalizes the server as an optimizer that uses utility-constrained stochastic aggregation to maximize client retention and global performance in heterogeneous federated learning.