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True Nexus and Pasqal target F&B protein performance with AI and quantum tech
Key takeaways
- True Nexus and Pasqal use neutral-atom quantum technology and AI to make protein functionality more predictable.
- The collaboration targets challenges in protein gelation and texture across alternative proteins and functional foods.
- The deep tech firms say computational protein design could reduce trial-and-error formulation for better-performing products.

True Nexus and Pasqal have used neutral-atom quantum technology to analyze protein structures linked to protein gelation — a process that turns liquids into gels and creates texture and structure in foods.
The extended partnership aims to make protein functionality more predictable, helping the F&B manufacturers design better-performing proteins for applications such as alternative proteins and functional foods.
Nearly 60% of global F&B consumers report actively incorporating more protein into their diets, according to Innova Market Insights. However, increasing protein supply alone is not enough to meet growing demand. It also requires improved protein performance in food systems
The deep tech firms hail the milestone as the first, decisive step toward making protein functionality “computationally accessible and, ultimately, programmable.”
True Nexus focuses on making protein functionality programmable for real‑world food applications by combining AI with quantum technology. France-headquartered Pasqal develops quantum processors to solve complex problems beyond the reach of traditional computers.
Closing the protein functionality gap
Dominik Grabinski, CEO of True Nexus, tells Food Ingredients First that the quantum technology advancement addresses the F&B sector’s challenge of “protein performance inconsistency in real food applications.”
Dominik Grabinski: Improved protein functionality can help reduce reliance on multiple ingredients, stabilizers, and processing solutions.
“Today, the industry is not short of protein sources. The real bottleneck is making these proteins deliver clear, reliable, and repeatable functionality: gelation, texture, binding, water-holding, mouthfeel, or stability.”
True Nexus can help improve, regulate, and better control the functionality of existing proteins, which is a “major relief for food processors and ingredient players.”
It can allow them to “buy, select, or develop proteins” with clearer and more predictable performance, even when these proteins are not yet fully equivalent to animal proteins.
“With Pasqal, the ambition is to close that gap further,” says Grabinski.
Decoding protein functionality
For F&B companies, the challenge lies deeper than finding new protein sources. Understanding the molecular mechanisms that determine how proteins behave during processing is important.
Gelatin — which offers unique performance in many food systems, especially in gelation, structure, and texture — is one example.
“Replacing it is not only a question of finding another ingredient. It requires understanding why a protein behaves the way it does, and how to improve that behavior under real processing and formulation conditions,” explains Grabinski.
“We are combining True Nexus’ AI protein intelligence, data, and market understanding with Pasqal’s neutral-atom quantum technology to open a new way of approaching this problem.”
The partnership aims to progress protein development beyond the trial and error stage.
AI-driven food protein design
Bloomberg Intelligence projects protein demand to approach US$1 trillion by 2030. However, a lack of design tools leaves plant-based, halal, and regionally grown protein sources largely untapped.
“Computational protein design can change the starting point of protein innovation,” Grabinski says. He flags that improving a protein is still largely based on long experimental cycles.
Computational protein design aims to move F&B innovation beyond trial-and-error ingredient testing.
“Companies test many ingredients, processing conditions, and formulations until they find something that works. This is slow, expensive, and often difficult to scale.”
AI and quantum technology can start from the desired function first, Grabinski explains. “For example, we need a protein to gel, bind, structure, or hold water in a specific food system. From there, we can better connect the molecular features, chemistry, processing conditions, and final food performance.”
This could help companies reduce blind testing, speed up better development choices, and bring new protein solutions closer to industrial reality faster.
Simplifying protein formulations
Grabinski sees the first F&B applications of quantum technology in products where proteins must deliver functionality beyond nutrition.
“This includes meat and seafood alternatives, dairy alternatives, confectionery, desserts, bakery, high-protein products, nutrition products, and hybrid foods. In all these applications, proteins can be essential for structure, texture, juiciness, mouthfeel, and stability.”
However, replacing animal proteins on a label is not the only benefit. “The real opportunity is to make next-generation proteins play the role animal proteins used to play naturally in simple and enjoyable foods,” says Grabinski.
He also points to growing consumer demands for less complicated formulations.
“Consumers want food that tastes good, has the right texture, and feels natural. Today, when proteins do not perform properly, formulators often need to compensate with multiple ingredients, stabilizers, or processing tricks.”
Sharpening focus on plant and fermentation-derived proteins emulating the proteins they are replacing can help the industry create “simpler, better, and more scalable products,” Grabinski concludes.













