QTL unifies expectation-value minimization with CVaR and Gibbs heuristics under one tunable operator, amplifying gradients in structured cases while preserving global minima and shifting the bottleneck to measurement variance.
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Pith papers citing it
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
SBQE encodes data via learnable shot distributions over initial states to form mixed quantum representations, achieving 89.1% accuracy on Semeion and 80.95% on Fashion MNIST without encoding gates.
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Quantum Tilted Loss in Variational Optimization: Theory and Applications
QTL unifies expectation-value minimization with CVaR and Gibbs heuristics under one tunable operator, amplifying gradients in structured cases while preserving global minima and shifting the bottleneck to measurement variance.
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Shot-Based Quantum Encoding: A Data-Loading Paradigm for Quantum Neural Networks
SBQE encodes data via learnable shot distributions over initial states to form mixed quantum representations, achieving 89.1% accuracy on Semeion and 80.95% on Fashion MNIST without encoding gates.