Advancing Molecular Simulations with Quantum Computing

From drug discovery to semiconductor fabrication, molecular simulations drive breakthroughs across industries. Yet, classical methods often struggle with complex reaction mechanisms—especially in capturing the finer details of atomic interactions.

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At C12, we’re developing a new generation of quantum processors using carbon nanotubes—designed for scalability and error resilience from the ground up. However, quantum computing isn’t just about hardware—it’s about solving real-world industrial challenges.

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In our latest research, we teamed up with Quantinuum, a global leader in quantum software and hardware, to explore how quantum computing can enhance chemical reaction modeling.

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Published on arXiv, our latest study applies quantum algorithms to atomic layer deposition (ALD)—a crucial process in semiconductor manufacturing. Our findings demonstrate how quantum computing can improve reaction modeling and overcome the limitations of classical methods like Density Functional Theory (DFT).

The Challenge: Modeling Chemical Reactions

Accurately predicting how and when chemical reactions occur is critical across industries. But classical approaches have well-known limitations:

• Complex Electron Correlations → Classical methods struggle to account for strong interactions between electrons, particularly in highly reactive states.
• Transition State Modeling → DFT, the standard tool in computational chemistry, works well for reactants and products, but fails at transition states (TS)—the fleeting moments where chemical bonds break and reform.

Where Quantum Computing Makes the Difference

Quantum computing provides a fundamentally different way to model chemical reactions. In our study, we focused on Fractional Orbital Density (FOD) plots, which visualize where classical methods diverge.

Figure 3 from our paper highlights this challenge:

• Before and after the reaction, electron interactions are well-behaved, and DFT performs reliably.
• At the transition state (TS), electron correlations peak (highlighted in orange), where classical approximations break down.

This is where quantum computing steps in—by handling strongly correlated systems, it offers a more accurate representation of reaction mechanisms, leading to better predictions of activation barriers and enhanced modeling of industrial processes.

Beyond ALD: A Step Toward Practical Quantum Chemistry

This study is an important step towards bringing quantum computing into industrial chemistry. As quantum hardware and algorithms improve, we anticipate even greater advancements in reaction modeling, making processes more efficient, cost-effective, and precise.

For those interested in the full technical details, you can read the scientific paper on arXiv here.

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