Why Leading Manufacturers Are Betting on Quantum Computing for Materials R&D The computers that design tomorrow's materials are hitting a wall, and a new technology is stepping in

Every advanced product starts with materials. The battery in an electric vehicle, the semiconductor in a smartphone, the alloy in a jet engine: each began as a simulation on a computer, long before anyone built a prototype. For decades, manufacturers have relied on computational simulation to predict how new materials will behave, saving years of trial-and-error in the lab.

The approach has its limits, however. Simulation accuracy is bounded by what the underlying hardware can compute, and for the most demanding materials challenges facing industry today, conventional computers can no longer model the underlying physics with sufficient precision.

Why conventional simulation falls short

Materials behave the way they do because of interactions happening at the atomic level: electrons orbiting nuclei, bonds forming and breaking, quantum interactions determining how atoms bind and repel. To design better materials, scientists need to simulate these interactions with extreme precision.

The problem is that the number of possible interactions grows exponentially with the size of the system. A simple molecule with a handful of atoms can be modeled accurately on a laptop. But the advanced materials that industry needs (high-temperature superconductors, next-generation batteries, novel crystalline compounds) involve so many interacting particles that even the world's most powerful supercomputers cannot capture the full picture. Scientists are forced to use approximations, and those approximations introduce errors that can mean the difference between a material that works and one that doesn't.

This isn't a theoretical concern. It directly affects how quickly companies can develop new products and how confident they can be in the results.

A different kind of computer for a different kind of problem

Quantum computers work fundamentally differently from conventional machines. Instead of processing information as simple ones and zeros, they use the principles of quantum mechanics, the same physics that governs how atoms and molecules actually behave. This makes them naturally suited to simulating materials at the atomic level.

The technology is still maturing. Today's quantum computers are not yet powerful enough to replace conventional simulation for most industrial problems. But they are already capable enough to deliver meaningful results on carefully chosen problems, and to help companies build the expertise they will need when more powerful machines arrive.

"Increasingly, we are seeing the sciences shift towards data-driven methodologies; however, data scarcity and fidelity remains as one of the most important issues. This is where quantum computers can help," says James Pegg, Research Scientist at QunaSys.

Five major manufacturers, spanning automotive, electronics, and advanced materials, have recognized this and are investing now, rather than waiting. Each of the engagements described in this paper was conducted by QunaSys, a quantum algorithm software company.

How companies are approaching it: structured, phased, practical

What stands out about these partnerships is not speculative ambition but practical discipline. None of these companies jumped straight into large-scale quantum research. Instead, they followed a phased approach:

Start with a focused study. Each engagement began by identifying a specific materials problem where conventional simulation falls short: a superconductor that needs to be modeled more accurately, a battery chemistry that requires higher-precision prediction, a crystalline material whose properties cannot be captured with existing tools. The goal of the first phase is not to solve the problem entirely, but to determine whether quantum approaches can add value.

Build a roadmap. Based on the initial results, the teams developed multi-year plans mapping out how their quantum capabilities will grow as the technology improves. These roadmaps are not aspirational wish lists; they are tied to specific technical milestones and, in several cases, have been adopted as official strategic references within the company.

Secure co-funding. Several of these partnerships leveraged government programs that co-fund quantum computing research. This reduces the financial risk for the company while signaling to internal stakeholders that the investment is backed by national technology strategy.

Publish and share. Multiple partnerships have produced peer-reviewed academic publications, an important form of validation that the approach works and that the results are credible. These publications also serve as recruiting tools, helping companies attract talent with quantum expertise.

What they have achieved so far

The results are already tangible, even at this early stage:

• One partnership produced a world-first calculation: a type of materials simulation that had never been performed on quantum hardware before. The result was published as a scientific preprint and demonstrated that quantum computers can handle computations that materials scientists actually need, not just toy problems.
• Another collaboration, running since 2022, has published results in a leading physics journal, showing that quantum methods can capture atomic-level effects that conventional approaches systematically miss. This is the longest-running enterprise quantum chemistry partnership in the country.
• A third company built a roadmap stretching to 2032, progressing through increasingly powerful quantum methods as the hardware improves. The roadmap has been formally adopted as an internal strategic reference and is being used to guide budget allocation.
• Two partnerships pioneered new intellectual property frameworks: legal structures that allow the quantum software to be reused across multiple clients while protecting each company's proprietary applications. These frameworks have become templates for other deals.

Why start now?

The most common question from companies evaluating quantum computing is: "Isn't it too early?" The experience of these five manufacturers suggests the opposite.

"To adapt to the rapidly changing technological landscape, modular hybrid quantum-classical architectures are well positioned to incorporate the latest advances," explains James Pegg. "These draw on the strengths of both paradigms as hardware and algorithms mature."

Quantum computing for materials simulation is not a single moment of breakthrough; it is a capability that builds over time. The companies that start now are developing internal expertise, building relationships with quantum specialists, establishing IP positions, and securing public co-funding. When hardware reaches the scale needed for industry-relevant problems (a milestone that leading hardware roadmaps now place within a few years), these companies will be ready to move immediately, while competitors will be starting from zero. "To understand how this impacts a particular field, designing these workflows now is critical," James Pegg adds.

The investment required is modest by industrial R&D standards. The first phase of a typical engagement runs for one to two years and delivers a working roadmap, initial results, and a clear understanding of where quantum computing fits in the company's broader R&D strategy. It is not a bet on an uncertain future; it is a structured, evidence-based process for building a new capability.

The bottom line

The five manufacturers in this case study are not futurists or early-stage startups. They are established industrial companies with decades of materials R&D experience. Their decision to invest in quantum computing is not driven by hype; it is driven by a clear-eyed assessment that conventional simulation is reaching its limits, and that a fundamentally different computing approach is needed to continue making progress.

For companies that depend on materials innovation (in automotive, electronics, energy, chemicals, or any field where the properties of matter determine the quality of the product), the question has shifted. It is less about whether quantum computing will matter, and more about how much preparation different companies will have had when it does.

Contact

To learn more about how QunaSys partners with manufacturers on quantum computing for materials simulation, please get in touch with our team.