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.
A competitive NISQ and qubit- efficient solver for the LABS problem
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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
-
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.
-
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.