From Scientific Perspective to Industrial Impact

Repsol is a global energy company headquartered in Spain, with activities across oil and gas, renewables, and low-carbon solutions. As part of its energy transition, the company is investing in advanced technologies to tackle complex industrial challenges.

Among these, quantum technologies are emerging as a potential tool to address some of the hardest problems in computational chemistry and materials science.

At Repsol, the exploration of quantum technologies is grounded in a clear objective: understanding when and where they can create real industrial value.

The company’s scientific lead for quantum technologies, Ricardo Enriquez, heads a multidisciplinary team of five experts working across sensing, metrology, and computing. Rather than developing quantum hardware, the team focuses on evaluating its relevance for real-world energy applications, particularly in the context of the energy transition.

His perspective reflects a balance between optimism and scientific rigor: “I am optimistic about quantum computing, but I need to stay grounded in scientific reality. The key is to sense when it becomes relevant.” This mindset defines Repsol’s broader approach of staying close to the technology while carefully assessing when it moves from promise< to practical impact. 

A Use-Case Driven Quantum Strategy – and Its Limits

At Repsol, quantum computing is approached pragmatically. The team is not focused on developing algorithms for their own sake, but on identifying concrete industrial applications. “We are not developers: we focus on use cases,” as Ricardo Enriquez puts it.

Their work spans several fronts, from a quantum-safe program addressing future cybersecurity risks to hands-on exploration of optimization, simulation, and machine learning problems. Through initiatives like the CUCO project, Repsol benchmarks classical and quantum approaches across different domains, building internal understanding through practical experimentation rather than speculation. A clear theme emerges: learning by doing, while staying closely aligned with real business needs.

This pragmatic approach also means confronting the current limitations of quantum computing head-on. In optimization, for example, the team has explored combinatorial problems and annealing-based approaches, but today’s systems remain far from the scale required for industrial deployment. As a result, attention is increasingly shifting toward areas where earlier impact may be possible, such as simulation.

Where Classical Methods Fall Short

Nowhere is this more relevant than in computational chemistry. Like much of the industry, Repsol relies heavily on Density Functional Theory (DFT), the standard tool for simulating molecular systems.

“DFT is the workhorse of computational chemistry,” Enriquez explains. “Quantum computing only becomes relevant when DFT doesn’t work.”

And there are important cases where it doesn’t. Reaction pathways, strongly correlated systems, and materials such as perovskites all expose the limits of DFT, for instance when magnetic interactions are involved. These limitations are not just theoretical; they directly affect the accuracy and reliability of industrial simulation workflows.

At the same time, Repsol is investing heavily in AI-driven molecular design, built on large internal datasets generated from DFT calculations. This creates a fundamental bottleneck. “Our generative AI relies on DFT data. But DFT is not accurate enough. This is where quantum computing might contribute substantially.”

Improving the fidelity of the underlying simulations could therefore unlock significantly better downstream performance, from more reliable predictions to more efficient design of catalysts and fuels.

Exploring New Approaches with QunaSys

To address these challenges, Repsol initiated a co-development project with QunaSys. Unlike many quantum projects, this effort is fully internally driven, underlining its strategic importance.

The objective is clear: to explore computational chemistry approaches capable of going beyond the limits of standard DFT. Two directions are central to this work.

The first is Quantum Monte Carlo (QMC), a method that is new to Repsol but offers a potential path toward higher-accuracy simulations. “We want to understand whether Quantum Monte Carlo makes sense for our problems,” says Enriquez.

The second is more exploratory, focusing on the relationship between quantum computing and stochastic methods. “What does quantum computing have to do with Monte Carlo? I am convinced there is a connection, especially through randomness.” This line of thinking also connects to broader research efforts around quantum stochasticity.

Alongside this, benchmarking plays a critical role. The collaboration spans several clean-energy material systems, including platinum-nickel catalysts for hydrogen production, CO₂ activation materials for carbon capture, and perovskite-based fuel-cell materials. Across these projects, Repsol compares classical high-performance computing methods with emerging quantum chemistry approaches such as Quantum-Selected Configuration Interaction (QSCI). Importantly, the hybrid quantum–classical workflow is designed to remain useful even on today’s limited quantum hardware, enabling meaningful calculations while quantum devices continue to mature.

The results are already encouraging. In several benchmark cases, QSCI achieves accuracy comparable to established approaches while offering a pathway to address systems where standard DFT struggles, particularly strongly correlated materials and complex magnetic interactions. While these methods are still evolving, the work demonstrates that quantum-enhanced approaches are already reaching meaningful levels of performance in real industrial contexts.

From Exploration to Industrial Readiness

Looking ahead, the potential applications are closely aligned with Repsol’s broader strategy in the energy transition, including catalyst development for hydrogen production, electrolyzer design, synthetic fuels, and next-generation materials for carbon capture and energy conversion. These are areas where improved simulation accuracy could translate directly into competitive advantage.

Selecting the right partner for this journey was therefore critical. Before starting the collaboration, Repsol evaluated several leading organizations in the quantum ecosystem. “We spoke with several different companies, and QunaSys stood out for a combination of technical depth and working style,” Enriquez explains. “We are a computational chemistry company ourselves, and we felt QunaSys was the best fit.”

A key differentiator was the collaborative approach, which allowed Repsol to start with hands-on training and progress into a focused joint project while maintaining flexibility and control. “When you work with large companies, you often lose control of your project. With QunaSys, we didn’t have that issue.”

For Repsol, quantum computing is not a distant bet, but an area of active exploration grounded in real industrial challenges. The goal is not premature adoption, but readiness. “We want to be early adopters, but in a meaningful way.” 

A Pragmatic Path Forward

The collaboration highlights a broader shift in the quantum industry: from abstract promise to application-driven exploration, from hype to measured, evidence-based progress, and from isolated research to deep industry partnerships.

For QunaSys, working with organizations like Repsol reinforces a core belief: the path to quantum value starts with real problems, and with the willingness to explore where classical methods reach their limits.