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Moderna Challenge
Optimization of mRNA Secondary Structure Prediction Using Quantum Computing
Moderna is a biotechnology company pioneering messenger RNA, or mRNA, medicines. This challenge explores a computational problem relevant to mRNA design: predicting and optimizing RNA secondary structure.
RNA secondary structure can influence molecular stability, translation efficiency, and manufacturability. Because the number of possible folds grows rapidly with sequence length, exploring the full folding landscape becomes computationally difficult. Participants will investigate whether quantum or quantum-inspired optimization methods can be used to reproduce known structures for short RNA sequences and analyze how the required computational resources scale
The Challenge
Develop a quantum or quantum-inspired approach for predicting mRNA secondary structure, with a focus on Minimum Free Energy, or MFE, folding.
Given an RNA sequence containing the nucleotides A, U, C, and G, teams will:
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Formulate the possible secondary structures as an optimization problem.
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Use a quantum or quantum-inspired method to identify low-energy candidate structures.
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Generate classical benchmark structures using ViennaRNA.
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Compare candidate structures against the classical MFE result.
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Evaluate the scalability and practical limitations of the proposed method.
Key Project Tasks
Teams should complete the following:
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Review RNA secondary structure, MFE folding, dot-bracket notation, and relevant quantum optimization methods.
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Use the ViennaRNA Python package to generate reference structures for short RNA sequences.
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Evaluate the energy of candidate structures and compare them with the reference MFE result.
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Formulate the folding problem for a quantum or quantum-inspired approach.
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Implement and benchmark the proposed method on small RNA sequences.
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Analyze qubit count, circuit depth, number of variables, runtime, and scaling limitations.
Possible Approaches
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QAOA
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VQE
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Quantum annealing
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Grover-style search
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Tensor-network-inspired methods
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Other suitable quantum or quantum-inspired optimization methods
Required Deliverables
Your final GitHub repository should include:
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Source code
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A clear README
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Installation and execution instructions
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Classical benchmark results
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Quantum or quantum-inspired implementation
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Results and analysis
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Scaling and quantum-resource analysis
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Documentation of assumptions and limitations
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A short presentation explaining the approach, findings, and future directions
The submission must be accessible and reproducible.
Optional Advanced Tasks
Teams may also:
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Explore formulations that include pseudoknots.
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Compare multiple quantum encodings.
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Evaluate the approach under sampling or hardware-inspired noise.
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Analyze trade-offs between qubit count and constraint enforcement.
Platforms & Tools
Participants may use any suitable platform, including:
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Qiskit
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PennyLane
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Cirq
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Amazon Braket
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IBM Quantum
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D-Wave
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Local quantum simulators
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ViennaRNA
Real quantum hardware is optional. Simulations are sufficient.
Judging Criteria
Projects will be evaluated based on:
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Correctness and clarity of the problem formulation
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Quality of the quantum or quantum-inspired approach
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Ability to approximate classical MFE benchmark structures
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Benchmarking quality
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Scaling and quantum-resource analysis
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Technical merit and creativity
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Code quality and reproducibility
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Communication and presentation quality
Data & Privacy Requirements
Use only public, synthetic, or randomly generated RNA sequences.
Do not use:
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Confidential Moderna data
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Patient or clinical data
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Proprietary sequence data
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Personally identifiable information
Team & Deadline
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Maximum team size: 3 participants
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Final submission deadline: August 7, 2026
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No deadline extensions will be offered.