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

TL;DR

This paper introduces a quantum-centric optimization framework for predicting the secondary structure of long mRNA sequences, combining quantum sampling with classical post-processing to address computational challenges.

Contribution

It presents two novel hybrid quantum-classical workflows based on QUBO formulations, demonstrating their effectiveness on IBM quantum hardware for sequences up to 60 nucleotides.

Findings

Successfully solved mRNA folding problems with up to 156 qubits on quantum hardware.

Validated scalability of the CVaR algorithm up to 354 qubits in noiseless simulations.

Demonstrated practical potential of hybrid quantum-classical methods for biological optimization.

Abstract

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.