Pauli Correlation Encoding for mRNA Secondary Structure Prediction: Problem-Aware Decoding for Dense-Constraint QUBOs

Abstract

Pauli Correlation Encoding (PCE) compresses $m$ binary variables onto $n=O(m^{1/k})$ qubits by mapping them to commuting Pauli correlators, but its continuous expectation values must be decoded into feasible binary solutions, a challenge for dense-constraint problems. We apply PCE to mRNA secondary-structure prediction, formulated as a densely constrained QUBO, and train with a QUBO-space sigmoid loss thatpreserves the QUBO penalty structure. For decoding, we introduce the Problem-Aware Guided Decoder (PAGD), which scores candidate variable commitments by combining marginal QUBO energy reduction with a trained expectation-value prior and constraint-aware feasibility pruning. On six benchmark mRNA sequences (30-60 nt, 50-240 variables, 7-14 qubits), PAGD with 100 restarts achieves 75-100 percent near-optimal recovery, defined as $P(\mathrm{gap}<1\%)$, for sequences up to 152 variables, compared with 0-30 percent for a sign-rounding plus local-search baseline. On the 240-variable instance, trained PAGD reaches 50 percent $P(\mathrm{gap}<1\%)$ at 200 restarts, outperforming untrained-circuit and random-expectation-value controls. Hardware-scale tests extend the pipeline to three 102-105 nt instances (694-745 variables, 172,000-193,000 pair constraints, 23 qubits) on IBM Heron processors. The circuits transpile SWAP-free into 480 native two-qubit gates at depth 256, and PAGD decoded gaps on QPU runs match or beat simulator means for all three instances, including exact CPLEX-optimum recovery for one sequence. These results show that PCE-trained priors can survive deployment to noisy superconducting hardware at biologically relevant scale.