IBM and Moderna Use Quantum Computer to Simulate Longest mRNA Structure Without AI

Researchers from IBM and Moderna have set a new milestone in molecular simulation by using a quantum computer to predict the secondary protein structure of a 60-nucleotide-long mRNA sequence — the longest ever modeled on quantum hardware without the aid of AI.

The experiment used IBM’s R2 Heron quantum processor, leveraging a conditional value-at-risk-based variational quantum algorithm (CVaR-based VQA) to simulate the complex folding patterns that give mRNA molecules their functional 3D shapes. Only 80 of the processor’s 156 available qubits were used for the test, but it still surpassed the previous record of 42 nucleotides for a quantum-based simulation.

Why This Matters

mRNA (messenger ribonucleic acid) carries genetic instructions from DNA to ribosomes, directing protein synthesis in cells. Understanding its precise folding — which can include intricate features like pseudoknots — is critical for developing more accurate mRNA-based vaccines.

Classical computing methods, including AI models like Google DeepMind’s AlphaFold, can handle larger sequences but often simplify the problem by ignoring such high-complexity features. This limits the accuracy of predictions, especially for medical applications.

By applying quantum computing, researchers aim to capture these fine structural details without sacrificing complexity, potentially leading to better vaccine design and faster drug development.

Experiment Highlights

Quantum Hardware: IBM R2 Heron QPU
Qubits Used: 80 (with potential to scale up to 156 in noisy conditions and 354 in noiseless simulations)
Algorithm: CVaR-based VQA, inspired by risk analysis and collision-avoidance models
Outcome: Accurate simulation of a 60-nucleotide mRNA secondary structure
Potential: Longer sequences and higher accuracy with more qubits and advanced error-correction techniques

The researchers also applied noise-reduction strategies to improve accuracy and ran initial tests suggesting the model could scale further as quantum processors and algorithms evolve.

While still in the experimental stage, the study — first presented at the 2024 IEEE International Conference on Quantum Computing and Engineering — demonstrates how quantum computing could complement, and in some cases surpass, traditional AI methods in molecular biology.

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