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.
Dimitris Alevras Mihir Metkar Takahiro Yamamoto Vaibhaw Kumar Triet Friedhoff Jae-Eun Park Mitsuharu Takeori Mariana LaDue Wade Davis Alexey Galda