Pauli Correlation Encoding with a trained problem-aware decoder achieves 75-100% near-optimal recovery on mRNA QUBO instances up to 152 variables and matches or exceeds simulator performance on IBM Heron processors for 694-745 variable cases.
Large-scale portfo- lio optimization using pauli correlation encoding
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Hybrid quantum-classical optimization for unit commitment uses Pauli-Correlation Encoding to solve multi-period schedules with up to 312 binary variables while satisfying load, ramping, and reserve constraints.
A quantum framework introduces C-Estimator and E-Estimator for classical covariance matrices using variational circuits, with regularization to ensure positive definiteness and mitigate barren plateaus, validated via simulations.
citing papers explorer
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Pauli Correlation Encoding for mRNA Secondary Structure Prediction: Problem-Aware Decoding for Dense-Constraint QUBOs
Pauli Correlation Encoding with a trained problem-aware decoder achieves 75-100% near-optimal recovery on mRNA QUBO instances up to 152 variables and matches or exceeds simulator performance on IBM Heron processors for 694-745 variable cases.
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Scaling Quantum Optimization for Unit Commitment via Pauli Correlation Encoding
Hybrid quantum-classical optimization for unit commitment uses Pauli-Correlation Encoding to solve multi-period schedules with up to 312 binary variables while satisfying load, ramping, and reserve constraints.
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Quantum Learning of Classical Correlations with continuous-domain Pauli Correlation Encoding
A quantum framework introduces C-Estimator and E-Estimator for classical covariance matrices using variational circuits, with regularization to ensure positive definiteness and mitigate barren plateaus, validated via simulations.