arXiv subjects

Wade Davis

Publications and source records attributed to Wade Davis.

5 recordsLinked to original sources

arXiv2026

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

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.

quant-ph↗

arXiv2025

Sample-based Quantum Diagonalization Methods for Modeling the Photochemistry of Diazirine and Diazo Compounds

Diazirines and diazo compounds are widely employed as photoreactive precursors for generating carbenes, key intermediates in chemical biology and materials science. However, computationally modeling their reaction pathways remains challenging due to a need for large active spaces and the requirement to accurately capture excited-state surfaces along with transition states and conical intersections. In this work, we utilize a hybrid quantum-classical workflow for investigating carbene formation in representative diazirine-diazomethane systems. Our approach leverages Sample-based Quantum Diagonalization (SQD) and its extended variant (Ext-SQD) for ground and excited-state analysis, combined with classical tools for geometry optimization, active-space selection, and diagnostic evaluation. Quantum computations were carried out on superconducting quantum processors, and results for both aliphatic and aryl-substituted diazirine-diazomethane pairs were benchmarked against established classical methods, including DFT, CCSD, CASCI, and SCI. SQD achieves accuracy surpassing the chemical accuracy threshold for nearly all stationary points on the potential energy surface of parent diazirine relative to the CASCI(12,10) reference, and remains close to chemical accuracy for phenyl-substituted diazirine in a (30,30) active space, with an average deviation of 1.1 kcal/mol relative to the SCI benchmark. SQD closely follows CASCI and SCI trends, showing consistent agreement. The findings demonstrate the promise of quantum computing frameworks in modeling photochemical transformations of electronically complex and pharmacologically relevant molecules.

physics.chem-ph↗

arXiv2025

Co-optimization of codon usage and mRNA secondary structure using quantum computing

Co-optimizing mRNA sequences for both codon optimality and secondary structure is crucial for producing stable and efficacious mRNA therapeutics. Codon optimization, which adjusts nucleotide sequences to enhance translational efficiency, inherently influences mRNA secondary structure - a key determinant of molecular stability both in-vial and in-cell. Because both properties are governed by the same underlying sequence, optimizing one directly impacts the other. To address this interdependence, we introduce a novel variational framework that simultaneously optimizes codon usage and secondary structure. Our method employs a dual-objective function that balances the codon adaptation index (CAI) and minimum free energy (MFE), incorporating variational parameters for codon selection. Leveraging a hybrid quantum-classical computational strategy and building on prior advancements in quantum algorithms for secondary structure prediction, we effectively navigate this complex optimization space. We demonstrate the feasibility of executing this end-to-end workflow on real quantum hardware, using IBM's 127-qubit Eagle processor. We validate our approach through both simulations and quantum hardware experiments on sequences of up to 30 nucleotides. These results highlight the potential of our framework to accelerate the design of optimal mRNA constructs for therapeutic and research applications.

quant-ph↗

arXiv2025

Towards secondary structure prediction of longer mRNA sequences using a quantum-centric optimization scheme

Accurate prediction of mRNA secondary structure is critical for understanding gene expression, translation efficiency, and advancing mRNA-based therapeutics. However, the combinatorial complexity of possible foldings, especially in long sequences, poses significant computational challenges for classical algorithms. In this work, we propose a scalable, quantum-centric optimization framework that integrates quantum sampling with classical post-processing to tackle this problem. Building on a Quadratic Unconstrained Binary Optimization (QUBO) formulation of the mRNA folding task, we develop two complementary workflows: a Conditional Value at Risk (CVaR)-based variational quantum algorithm enhanced with gauge transformations and local search, and an Instantaneous Quantum Polynomial (IQP) circuit-based scheme where training is done classically and sampling is delegated to quantum hardware. We demonstrate the effectiveness of these approaches using IBM quantum processors, solving problem instances with up to 156 qubits and circuits containing up to 950 nonlocal gates, corresponding to mRNA sequences of up to 60 nucleotides. Additionally, we validate scalability of the CVaR algorithm on a tensor network simulator, reaching up to 354 qubits in noiseless settings. These results demonstrate the growing practical capabilities of hybrid quantum-classical methods for tackling large-scale biological optimization problems.

quant-ph↗

arXiv2024

mRNA secondary structure prediction using utility-scale quantum computers

Recent advancements in quantum computing have opened new avenues for tackling long-standing complex combinatorial optimization problems that are intractable for classical computers. Predicting secondary structure of mRNA is one such notoriously difficult problem that can benefit from the ever-increasing maturity of quantum computing technology. Accurate prediction of mRNA secondary structure is critical in designing RNA-based therapeutics as it dictates various steps of an mRNA life cycle, including transcription, translation, and decay. The current generation of quantum computers have reached utility-scale, allowing us to explore relatively large problem sizes. In this paper, we examine the feasibility of solving mRNA secondary structures on a quantum computer with sequence length up to 60 nucleotides representing problems in the qubit range of 10 to 80. We use Conditional Value at Risk (CVaR)-based VQE algorithm to solve the optimization problems, originating from the mRNA structure prediction problem, on the IBM Eagle and Heron quantum processors. To our encouragement, even with ``minimal'' error mitigation and fixed-depth circuits, our hardware runs yield accurate predictions of minimum free energy (MFE) structures that match the results of the classical solver CPLEX. Our results provide sufficient evidence for the viability of solving mRNA structure prediction problems on a quantum computer and motivate continued research in this direction.

quant-ph↗