![\"IBM\u2019s](\"https:\/\/www.newsbeep.com\/au\/wp-content\/uploads\/2025\/07\/54020604001_bdc185024e_c.jpg\")
<\/p>\n<\/p>\n
IBM\u2019s second-generation, 156-qubit Quantum Heron processors can run quantum circuits with up to 5,000 two-qubit gate operations using Qiskit. (Credit: Ryan Lavine for IBM)<\/p>\n
In 2022, Moderna brought in approximately $19.26 billion in revenue, largely thanks to its groundbreaking Spikevax COVID-19 vaccine. In January 2025, the company is projecting revenue of $1.5 billion to $2.5 billion<\/a>. To reverse this downturn, the company is pushing to broaden mRNA\u2019s applications into cancer, rare diseases and other areas, but that requires cracking tough computational barriers in sequence design.<\/p>\n The therapeutic potential of mRNA extends far beyond COVID-19, or RSV, another infectious disease for which it scored an FDA approval in 2024. The ability to precisely instruct cells to produce specific proteins opens the door to a new class of medicines for a vast range of diseases. Yet designing the optimal mRNA sequence for a given therapeutic protein is a monumental computational challenge. Consider that the human body has more than 100,000 types of proteins, generated from about 20,000 genes through various modifications, and each protein is derived from mRNA. For any one of those proteins, the number of possible mRNA sequences creates a complex optimization problem that strains the limits of classical computation. The molecule\u2019s secondary structure, the way it folds into stems, loops and bulges, compounds the problem, determining how efficiently the mRNA translates into protein, how stable it remains in the body, how it interacts with cellular machinery, and whether it triggers unwanted immune responses.<\/p>\n \u201cQuantum computing lets us frame the mRNA-folding question as a giant puzzle in which every possible pattern of base-pairing is scored by its predicted free-energy,\u201d says Wade Davis, Moderna\u2019s senior vice president of digital. \u201cIt\u2019s natural to look at quantum as another approach that could be complementary,\u201d adds Sarah Sheldon, senior manager, quantum theory and capabilities at IBM. In its work with IBM, Moderna is applying this quantum-centric approach to the optimization of mRNA sequences. The aim is to expand the diversity of candidate molecules its design pipeline can produce for applications ranging from new vaccines to personalized cancer treatments.<\/p>\n The mRNA molecule naturally tends to adopt the structure with the lowest free energy, which is its most stable state. The central difficulty, as Davis puts it, is that \u201cThe computational bottleneck lies in searching for optimal solutions across an astronomically large design space.\u201d While evaluating the quality of a single candidate sequence is relatively easy, finding the best ones is substantially more involved.<\/p>\n Cracking the computational hurdles in mRNA design<\/p>\n The mRNA folding problem is a good fit for quantum computers not because of \u201cbig data,\u201d but because of its complexity. According to Sheldon, the ideal quantum problem has an \u201cunderlying structure that makes it hard classically at a relatively small size.\u201d<\/p>\n The mRNA folding challenge exhibits exactly this trait. As the nucleotide sequence gets longer, the computational difficulty scales exponentially. Sheldon notes that while current work on 60-nucleotide sequences can be verified with classical computers, the problem \u201cgets hard very quickly\u201d beyond that point. This scaling challenge is what makes a quantum approach so promising for Moderna\u2019s long-term goals. Davis emphasizes the future potential, stating the approach shows how maturing quantum devices could help scientists explore a \u201cvastly broader landscape of inherently stable mRNA designs more quickly than classical methods alone.\u201d<\/p>\n Quantum joins the biotech toolbox<\/p>\n Moderna\u2019s strategy is one of early adoption, reflected in its move to hire dedicated quantum specialists, including professionals managing quantum algorithms and applications. This philosophy signals a trend where companies build in-house quantum skills. The trend underscores the growing need for hybrid experts who can connect industry-specific challenges to quantum algorithms.<\/p>\n The collaboration integrates quantum and classical systems into a single workflow. As Sheldon, explains, the process is about \u201cfiguring out where you need a quantum computer within your workflow,\u201d using it for the specific computational bottlenecks that classical machines struggle with. This hybrid model is central to what IBM calls the \u201cera of quantum utility.\u201d For Moderna, the primary goal is building institutional knowledge, mastering the art of translating complex biological problems into a language quantum computers can solve.<\/p>\n This strategy creates a clear division of labor.\u00a0While partners like IBM focus on developing better hardware, Moderna is mastering the application of that hardware to real problems. This will pay off in stages. In the near term, Davis explains, \u201cthe value is cutting search time on known targets.\u201d The longer-term hope is that a \u201cdiversified sequence set could surface novel designs that classical heuristics may overlook.\u201d<\/p>\n Inside the hybrid quantum process<\/p>\n Tackling the mRNA structure problem begins by translating it into a format a quantum computer can understand. The team maps the biological challenge onto a Quadratic Unconstrained Binary Optimization (QUBO) problem, a mathematical model used to represent complex decision problems as binary variables and quadratic objectives.<\/p>\n The method is inherently hybrid, using a Variational Quantum Eigensolver<\/a> (VQE) that creates a feedback loop between quantum and classical machines. A quantum circuit is run, and the results are fed to a classical optimizer, which then adjusts the parameters for the next run. This iterative partnership is crucial, as \u201cclassical computers are very good at a lot of things,\u201d Sheldon notes.<\/p>\n It\u2019s really about figuring out where you need a quantum computer within your workflow. \u2014Sheldon<\/p>\n To refine this process, the team incorporated Conditional Value at Risk (CVaR)<\/a>, a technique adapted from finance. Rather than averaging all possible outcomes from the quantum computer, CVaR focuses the algorithm on the most promising results. As Sheldon explains, it directs the search toward the \u201ctail end of the distribution that has lower energies.\u201d In other words, it represents the most stable molecular structures while allowing the optimization to converge faster.<\/p>\n This entire process yields a shortlist of promising nucleotide sequences. These are then rigorously validated. According to Davis, for smaller sequences, they perform \u201cclassical cross-checks\u2026 we confirm that the quantum routine reproduces the classical optimum.\u201d For larger problems, they \u201ccompare the quantum-generated answer with the strongest classical results available,\u201d ensuring the results are reliable.<\/p>\n Quantum\u2019s potential impact on medicine<\/p>\n In 2024, the collaboration simulated mRNA sequences of up to 60 nucleotides on quantum hardware. Published in an arXiv preprint<\/a>,\u00a0the study demonstrated that a Conditional Value at Risk (CVaR)\u2013based variational quantum algorithm could reliably reproduce minimum free energy secondary structures, matching the results of commercial classical solvers such as CPLEX<\/a>. This scale is valuable because the results can still be verified against classical computers, providing a crucial benchmark. As the technology scales, Davis notes it could allow researchers to \u201ctreat ~100-nucleotide segments,\u201d a size large enough to begin competing with state-of-the-art classical methods.<\/p>\n The ultimate goal is a quantum-enabled pipeline that could improve treatments and \u201cmake ultra-rare or even personalized mRNA treatments more accessible.\u201d Yet Davis is quick to caution that these gains are a \u201chopeful research direction rather than an operational capability,\u201d as they depend on future advances in hardware and error mitigation.<\/p>\n Realizing this vision requires overcoming several concrete hurdles. Davis identifies key challenges, including the scarcity of hardware, the skill gap requiring user-friendly tools for scientists, the need for regulatory traceability in an auditable format, and ensuring results can integrate cleanly with existing AI and molecular dynamics stacks.<\/p>\n Despite these obstacles, this work is creating a path forward. Sheldon sees the current era as a unique opportunity for innovation, where practical hardware allows researchers to rigorously test new ideas for both quantum algorithms and their real-world application.<\/p>\n Quantum\u2019s reach beyond biotech<\/p>\n The current work is taking place in what Sheldon describes as a \u201cpre-fault-tolerant world,\u201d where today\u2019s quantum processors rely on heuristic methods and error mitigation rather than full error correction. Despite these limitations, the collaboration is focused on foundational problems, quantum simulation and optimization, that have broad applications across industries like finance, logistics, and materials discovery.<\/p>\n Despite these limitations, there are broad cross-industry applications being actively explored. As Sheldon explains, \u201cfrom my end, I look at it as, what are the algorithms that we\u2019re developing, and what are the types of problems those algorithms can address?\u201d Two major areas of focus are quantum simulation and optimization. There is a significant effort in \u201cthe simulation of quantum systems like chemistry and materials,\u201d which has direct relevance for \u201cdrug discovery problems, but also materials discovery.\u201d Additionally, \u201coptimization problems pop up all over the place. Obviously finance, but also logistics. There\u2019s a lot of hard optimization problems out there.\u201d<\/p>\n Looking toward the future, IBM has a detailed quantum roadmap. This plan includes the development of the IBM Quantum Heron processor, which features lower error rates, and outlines a progression toward quantum-centric supercomputing that integrates with High-Performance Computing (HPC) resources. A key long-term goal of this roadmap is achieving eventual error correction capabilities.<\/p>\n Sheldon believes this synergy between hardware development and practical problem-solving is what defines the current innovation opportunity. \u201cAnd I think that\u2019s really what is exciting about the coming years,\u201d she concludes, \u201cis that we have hardware where we can really do this testing and understanding of the scaling of problems\u2026 there\u2019s a lot of opportunity there for new ideas on the problem side and the algorithm side.\u201d<\/p>\n