By Massera Winigah and Ksenia Etcheverry  Reading time: 15 minutes

Twenty years ago, digital twins were still an emerging idea. Over time they matured into a practical workhorse for manufacturing and the design-build industries and are now well on the way to being deployed in the energy sector, once IoT sensors and industrial software mature enough to simulate complex systems like power plants, grids, or oil refineries in real time.

Today, the new frontier gaining momentum is quantum computing, a technology with the potential to solve problems that even the most powerful supercomputers struggle to crack: finding the most efficient configurations in massive energy systems, simulating new materials at the atomic level, accelerating the discovery of better catalysts and battery chemistries, and so much more. 

The shift from research to real-world application is well underway, and this comes at a time when a new approach to energy production and consumption is needed the most.

Why is this the year of quantum computing? 

The short answer: We are reaching the limits of our energy demands, pressures, and computational capabilities.

To put this in the right context, the summer of 2025 brought record-breaking heatwaves across Europe, sending electricity demand soaring just as hydropower output fell. For now, efficiency tweaks and digital twins help manage the grid, sure, but the complexity of balancing renewables, markets, and extreme weather calls for new applications that think not only faster but differently. 

Energy companies now face a defining moment when they must deliver reliable, affordable power to a growing global population while also cutting emissions fast enough to meet climate commitments. Traditional computing is reaching its limits in addressing these challenges.

This is where we can look to quantum computing as an option to help address the energy sector’s most complex challenges through computational capabilities that classical systems simply cannot match. 

Quantum computing becomes the new frontier for the energy industry

In a decade defined by volatility and rapid technological shifts, the risk of waiting is being left behind.

By 2025, major energy players, including E.ON, Shell, ExxonMobil, and Aramco are already piloting quantum projects with past Hello Tomorrow Deep Tech Pioneers (startups selected by an expert jury through the Hello Tomorrow Challenge), such as Pasqal and IQM Quantum Computers, to gain a competitive edge in areas such as grid optimisation and new materials discovery.

They are not the only ones. Figures from SRI International, an independent R&D institute, show that private investment in quantum technologies hit $2.6 billion (€2.42 billion) in 2024 alone. 

At the same time, a McKinsey report published in June 2025 shows that public investment also rose by 19% between 2023 and 2024. 

This increase in investment and “faster-than-expected innovation” could push the quantum market to $100 billion (€93 billion) within the next decade. 

For innovation leaders in the energy sector, the question is no longer whether quantum will be relevant, but who will seize the competitive edge first. 

But, the bigger story is that…

The future of quantum computing in the energy transition extends well beyond optimising grid management. It is increasingly being shaped by advances in materials and system design. 

For example, quantum computing has the potential to enable entirely new business models, from developing next-generation batteries to improving climate modelling.

And quantum computing also matters most in these areas:

Five promises of quantum computing for the energy sector

1.Optimise grids and forecast demand like never before

Taking into account the intermittence of renewable energy, rising demand, and limited storage capacity, the Iberian Peninsula blackout in April 2025 exposed just how vulnerable modern power systems remain and how quantum computing could eventually change that. The collapse began with a voltage surge the grid could not absorb due to low inertia, weak coordination, and slow reserves. 

Maybe quantum computing could not have stopped it in real time, but it could have helped prevent it as well.  Quantum algorithms excel at tackling complex, multi-variable optimisation problems far faster than classical ones. Applied to grid planning, they could simulate millions of “what-if” scenarios in minutes, predict instability points, and optimise dispatch and reserves under uncertainty.

Furthermore, hybrid quantum-classical models could also help predict demand by crunching weather patterns, EV charging behaviour, and household consumption, thus making forecasts more precise and reducing the risk of blackouts.

Ongoing projects: 

– National Renewable Energy Laboratory (NREL), in collaboration with Atom Computing and others, has developed an open-source, vendor-neutral interface that links quantum computers directly with power-grid simulation and control equipment. The “quantum-in-the-loop” setup enables researchers to test quantum algorithms in realistic grid environments, such as coordinating electric vehicle charging or switching power sources.

– More recently, in the framework of the U.S. Department of Energy GRID-Q project, IonQ and the Oak Ridge National Laboratory (ONRL) reported successfully using a hybrid quantum-classical system to optimise electricity generation scheduling. Known in the industry as the Unit Commitment problem, known in the industry as the Unit Commitment problem.  According to the US Energy Information Administration, more than 60% of energy used in electricity generation is currently lost, stating this as pointing to a significant opportunity for waste reduction through improved scheduling.

–Pasqal’s (Deep Tech Pioneer 2019) work with EDF aims to use quantum computing to improve renewable energy integration and manage new sources of electricity demand like EV charging. By simulating weather and production variables with high accuracy, their algorithms help forecast renewable availability and reduce reliance on backup systems. They are also testing quantum-based optimisation for EV charging schedules to prevent local grid congestion and improve energy distribution efficiency.

 2.Design better batteries using quantum calculations and quantum metrology 

The chemistry of advanced batteries is messy: electrolytes, electrodes, and ion interactions involve millions or even billions of variables that are costly or even impossible to simulate fully.

However, by simulating materials at the atomic level, quantum systems should be able to predict how batteries will behave in terms of energy density, stability, charging speed, and lifespan.

Quantum metrology uses quantum-based sensors to observe atomic-scale changes in real time, for example, monitoring how lithium ions move during charging. Combined with quantum simulations, these measurements could dramatically accelerate the development of new battery chemistries by reducing the need for slow and costly trial-and-error experiments in the lab.

Ongoing projects: 

Volkswagen and IQM Quantum Computers (Hello Tomorrow’s Deep Tech Pioneer 2019) have demonstrated that next-generation quantum methods can simulate battery materials with far greater precision while requiring far fewer qubits than anticipated. In practical terms, this means that researchers can model the behaviour of key components inside a battery, including the chemical reactions that determine safety, efficiency, and degradation, without requiring a full-scale, fault-tolerant quantum computer. If this approach is further validated, it could accelerate the development of longer-lasting, faster-charging, and lower-cost batteries.

3. Discover new materials for decarbonising energy

Quantum computing isn’t just for batteries; it can accelerate the discovery of the materials needed across the whole energy value chain, including:

-In green hydrogen, quantum simulations can help identify and optimise catalysts that split water more efficiently, lowering energy use and cost. The same approach can improve membrane materials inside electrolysers, which today are expensive, degrade quickly, and depend on scarce metals..

-Quantum methods are also being explored in solar manufacturing to model new semiconductor materials and protective coatings that improve efficiency and lifespan, especially in perovskite and tandem-cell designs. 

-Quantum simulations are also emerging in carbon capture, helping to design new sorbents and porous materials that bind and release CO₂ more efficiently.

-For synthetic fuels and green ammonia, they can explore completely new catalyst surfaces and reaction pathways that classical models can’t handle.

-In next-generation energy systems such as fusion or high-temperature reactors, quantum methods may help design the heat-resistant and superconducting materials those technologies depend on.

By integrating quantum simulations with experimental data, energy companies can potentially reduce research and development cycles, accelerating the transition to a decarbonised energy future.

Ongoing projects: 

ENGIE is investigating the potential of quantum computing to enhance various energy technologies. The company has highlighted quantum computing as a promising tool for developing more efficient algorithms to address complex challenges in areas such as green hydrogen production, carbon capture, and renewable energy systems. Although ENGIE notes that quantum computing remains an immature and complex technology that cannot yet be used in industrial settings, progress is ongoing. Future hardware development is expected to focus on increasing the number of quantum bits and reducing errors in calculations.

4.Price and hedge energy contracts with greater precision

In liberalised energy markets, prices fluctuate rapidly due to unpredictable weather, fuel costs, demand spikes and the intermittent nature of renewable energy, among other factors. These interdependencies make accurate pricing and hedging extremely challenging from a computational perspective. Even minor miscalculations can cost utilities millions or disrupt market stability.

Ongoing projects: 

E.ON’s collaboration with IBM explores how quantum computing could change energy market operations. By simulating complex market conditions (weather, consumption patterns, other interdependencies) with far greater fidelity, quantum algorithms can evaluate countless pricing and risk scenarios simultaneously, a task that would overwhelm classical systems. While still in the experimental phase, this approach points to a future where energy providers can set prices, manage exposure, and balance renewable uncertainty with unprecedented accuracy and speed.

5. Streamline complex logistics of fuel transport & global routing 

Quantum computing is also relevant to the physical movement of energy, not just electrons/data/trading models.

In fact, in the energy sector, logistics presents a significant challenge: a single company may coordinate hundreds of liquefied natural gas (LNG) vessels, each making thousands of voyages annually to deliver critical fuel worldwide. 

Even when simplified to just a few dozen ships, the number of possible routing combinations for thousands of vessels becomes astronomical. Dr. Vijay Swarup, Senior Director of Climate Strategy and Technology at ExxonMobil, explains that the number of potential routing combinations can exceed the number of atoms in the observable universe.

This is where quantum computing can be used to explore many possible routes simultaneously, using the principles of superposition and entanglement. This allows them to identify the most efficient shipping paths faster, potentially reducing fuel use, cutting costs, and lowering carbon emissions across global energy logistics.

Ongoing projects:

ExxonMobil’s collaboration with IBM offers an early glimpse of this potential. The two companies have explored how quantum algorithms could handle the immense complexity of global maritime routing, and while still at the research stage, their findings suggest that quantum optimisation could make large-scale energy logistics faster, leaner, and less carbon-intensive once the technology matures.

There are challenges of quantum adoption in the energy sector

The biggest obstacle to quantum adoption in the energy sector is not merely enthusiasm; it’s maturity. While technology is maturing quickly, today’s quantum hardware is still noisy and too small to run large-scale industrial problems. Error rates, short coherence times and the need for millions of logical (not just physical) qubits mean we are still a few years away from fully fault-tolerant machines.

Even if the hardware were ready, the software stack remains immature. Many algorithms that look promising on paper cannot yet be executed at useful scale, and hybrid quantum-classical workflows are still in the experimentation phase. The industry is still to confirm which algorithmic approaches will deliver practical quantum advantage for real-world energy use cases, as opposed to academic demonstrations.

Finally, adoption is slowed by organisational and economic barriers. Quantum advantage will first appear upstream, in materials discovery and infrastructure planning, not in short-term operational KPIs. That creates a misalignment with how most energy companies invest: they optimise today’s assets, while quantum creates value in tomorrow’s technologies. Furthermore, talent is also scarce, especially at the intersection of energy engineering and quantum science, making it difficult for incumbents to internalise capabilities at pace.

So why is it still the right time to bet on quantum? 

According to the 2024 report titled “The Long-Term Forecast for Quantum Computing Still Looks Bright”, by BCG, it shows that quantum computing will create between €418.12 billion and €789.8 billion of economic value, sustaining a market ranging from €83.62 billion to €158.07 billion for hardware and software providers by 2040. But for energy companies, the big question is whether quantum computers can actually do things better than today’s machines. 

Experts in the field call this the “quantum economic advantage”. This is another way to say that quantum computing should only be used when it’s technically possible and it’s clearly faster or smarter than traditional computing. This sweet spot usually shows up in really complex problems, like the ones outlined in the section above.

The industry is now moving towards systems with hundreds of qubits — enough to begin addressing real-world energy and logistics issues. Among the leading candidates, neutral-atom quantum architectures stand out because they can increase the number of qubits while consuming relatively little power (roughly 2.6 kW), which is far lower than that required by large classical supercomputers or some competing quantum platforms. If this energy-efficient scaling continues, neutral-atom quantum architectures could become one of the most practical solutions for commercial deployment.

As Neil Thompson, research scientist at MIT Sloan and MIT CSAIL explained it, “Think of it like a race in getting from point A to point B, and the algorithm is the route. If the race is short, it might not be worth investing in better route planning. For it to be worth it, it has to be a longer race.”

So why is it the right time to invest in quantum technologies while most projects in the energy sector are still in the experimental phase? 

1. First-mover advantage

According to BCG’s aforementioned report, companies entering early could capture up to 90% of the value in quantum’s “winner-takes-most” market. For now, most incumbents use quantum as a talking point while continuing to optimise their existing assets with classical HPC and AI. But the real value of quantum in energy does not lie in incremental optimisation — it lies upstream, in the discovery of new materials, catalysts, membranes, and infrastructure designs that will define tomorrow’s energy system. That means the competitive advantage will appear first in places incumbents are not yet looking.

This is a textbook Innovator’s Dilemma: the technology starts out immature and commercially “irrelevant,” so large players ignore it, while new entrants quietly build capabilities in the future stack. By the time quantum delivers a step change in performance, the companies that invested early in quantum-native materials and planning models may be positioned to outpace incumbents who optimised the old system too long.

2. Strategic partnerships

Energy companies across Europe, the US, and globally are forging strategic alliances with quantum startups, targeting quantum-based machine learning models for the energy sector. 

As Gary Pisano shows in Science Business, the core scientific knowledge, infrastructure, and tacit capabilities needed to bring a breakthrough to market sit in different institutions: universities, public labs, startups, corporates, standards bodies. No firm can single-handedly internalise that entire system. 

This is exactly what we’re now seeing in quantum for the energy sector. A utility cannot simulate a grid alone without quantum hardware specialists; a hydrogen or CCUS player cannot unlock new catalysts without upstream scientific partners; materials innovation depends on access to both computation and domain expertise. 

The competitive edge will not come from “owning” the technology outright, but from embedding early in the ecosystems where the scientific learning is compounding. In science-based innovation, the winners are those who partner upstream, before the product is finished, because that is where the capabilities and the learning curve are being shaped.

3. Preparing for the post-2030 quantum advantage

We are also looking toward future capabilities where many organisations are establishing quantum-readiness programs. As an example, Microsoft announced 2025 as the target year for becoming quantum-ready, whilst McKinsey projects approximately 5,000 operational quantum computers by 2030. 

So don’t say we did not warn you!

Advice for corporate leaders in the energy sector

For energy executives looking to capitalise on the future of quantum computing, practical preparation should begin immediately:

1. Develop a fundamental understanding of what quantum computers can and cannot do—both today and tomorrow—as both quantum promises and current challenges to implementations remain important.

2. Validate use cases through proofs of concept to demonstrate quick wins and secure budget approvals. Indeed, most, if not all, of the quantum projects led by energy giants and featured in this article are still pilots. 

3. Establish the right quantum partnerships, as no single player possesses all necessary quantum capabilities (not yet, at least), and/or consider joining established quantum networks or consortia.

As Lucas Maurice, Senior Innovation Consultant at Hello Tomorrow Consulting, explains, the industry is maturing: existing systems already support pilot projects across sectors, offering early evidence of quantum computing’s power to tackle complex problems. Energy companies and other industrial players should explore how quantum can deliver an advantage over classical computing and AI to better position themselves for the arrival of fully operational quantum systems.

The bottom line is that…

If your company is feeling the squeeze between rising demand and strict emissions goals, you might find that traditional systems just aren’t enough. There are quantum companies working on great solutions for the energy sector.

We might not have full-scale machines for years, but there’s a strong business case for putting money into them now. When you develop quantum-ready algorithms, build partnerships, and run pilots now, you’ll gain experience and stay ahead of the curve. In a “winner-takes-most” market, those who wait until the last minute risk losing out on the most value.

For energy leaders, the near-term path is all about hybrid quantum-classical approaches, blending quantum tools with existing systems.

So, in the next ten years or so, the energy companies that get quantum literacy right now are going to be way ahead of the game. That makes current investments a matter of not if, but when.

Is your company looking to identify emerging trends and explore new markets in the energy sector? Is your R&D looking for missing technological bricks and IP? 

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