Quantum for Energy and Utilities 2026

Top near-term quantum solutions in power and grid infrastructure FIGURE 7 76% 72% 64% 56%Quantum computing – large-scale optimization of high-voltage grid flows Quantum communication – quantum key distribution for ultra-secure grid communication Quantum computing – cross-border power trading optimization Quantum sensing – detect faults and line stress in real timeTransmission 75% 67% 58% 54%Quantum computing – local optimization of distributed energy resources Quantum computing – EV charging scheduling at grid scale Quantum communication – smart meters and IoT nodes secure Quantum sensing – detect anomalies in local distribution networksDistribution 77% 54% 54% 50%Quantum computing – simulate new battery chemistries beyond lithium-ion Quantum communication – monitor degradation of large-scale battery farms Quantum computing – catalysts for hydrogen electrolysis and storage Quantum sensing – monitor degradation of large-scale battery farmsStorage Source: Community survey, World Economic Forum’s Quantum for Energy and Utilities Working Group, February 2026. CASE STUDY 10 Quantum computing Modelling battery cathode materials with quantum computing to design better lithium-ion batteries Cathode materials largely determine how well a lithium- ion battery performs and how it degrades over time. Many cathodes are metal-oxide materials where the chemistry shifts as the battery charges and discharges, which makes them hard to predict accurately with today’s computer models. Conventional simulations often have to choose between being fast but less reliable or more accurate but too slow and costly to use broadly, so these cathode materials are a promising place to try quantum computing as the technology becomes more capable. Ford and Quantinuum publicly described a workflow to study a representative cathode material (LiCoO2) by breaking the problem into smaller “building-block” models that capture key parts of the chemistry during charging and discharging. The goal was not to fully simulate an entire battery particle, but to show a practical way quantum computing could be used inside a materials research process, running the quantum step where the hardest chemistry shows up, and comparing the results against established classical methods to see where it helps and where it falls short. Key benefits reported so far are that the work shows a repeatable research approach (not just a one-time demo), makes it clear what still needs to improve before it can scale up (today’s quantum computers are still limited), and outlines a practical path toward more accurate battery- material predictions as the technology gets more reliable. Quantinuum’s newer results on more dependable quantum calculations are relevant because they aim to solve the biggest hurdle, getting answers stable and accurate enough to be useful, but the overall state is still early-stage R&D.15 24 Quantum for Energy and Utilities: Key Opportunities for Energy Transition