mRNA secondary structure prediction using utility-scale quantum computers

TL;DR

This paper demonstrates that current utility-scale quantum computers can accurately predict mRNA secondary structures with sequence lengths up to 60 nucleotides, matching classical methods and showing promise for future RNA therapeutics design.

Contribution

It presents the first feasibility study of using quantum computers for mRNA secondary structure prediction with problem sizes up to 60 nucleotides.

Findings

Quantum hardware accurately predicts minimum free energy structures.

Results match classical solver CPLEX outputs.

Demonstrates viability of quantum approaches for RNA structure prediction.

Abstract

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.