Moderna and IBM have published a new case study demonstrating the application of quantum and classical computing in predicting mRNA secondary structures, a complex step in designing mRNA-based medicines. This collaboration is an extension of their ongoing partnership to explore how emerging quantum algorithms can address the limitations of classical computing in mRNA development.
The joint teams have applied new algorithms to enhance the ability of near-term quantum computers to tackle this challenge. Their research includes the application of Conditional Value at Risk (CVaR), a risk-assessment technique, to improve the performance of Variational Quantum Algorithms (VQAs) for finding optimal solutions to complex optimization problems. This approach, which functions as a classical post-processing step, aims to mitigate variance and focus the optimization process on lower-energy solutions, efficiently utilizing quantum hardware like the IBM Quantum Heron processor. The collaboration also explores instantaneous quantum polynomial (IQP) circuit-based quantum optimization for scaling problem sizes.
This work represents an advanced application of quantum computers in life sciences, illustrating their potential to transform and accelerate drug discovery. The research achieved a record-setting scale for a quantum secondary structure simulation in 2024, involving up to 80 qubits and mRNA sequence lengths of up to 60 nucleotides. Subsequent work applied the methodology to problem sizes involving up to 156 qubits and 950 non-local gates. The collaboration’s objective is not to replace classical computing but to build a near-term quantum-enabled biotechnology pipeline, augmenting classical computation for specific workflow bottlenecks and accelerating breakthroughs across chemistry, materials science, optimization, and drug discovery.
Read the IBM case study here, the IEEE paper here, and the technical pre-print paper on arXiv here.
July 18, 2025
