Quantum for Energy and Utilities 2026

annealing) target these NP-hard search spaces by exploring many candidate configurations efficiently, with the practical goal of finding better solutions under tight time limits, not perfect global optima. If validated, even small percentage improvements can translate into meaningful fuel savings and higher asset utilization at scale. Pipeline flow optimization Moving gas or oil through transcontinental pipelines means continuously tuning compressor stations and valve settings to cut fuel use while still satisfying pressure limits and meeting contract delivery requirements. Hybrid classical-quantum computing solutions are expected to provide improved real-time decision making to better optimize pipeline flow. LNG shipping and maritime logistics Optimizing liquefied natural gas (LNG) tanker routes is among the most computationally difficult challenges in the industry. It requires coordinating a global fleet while accounting for changing boil- off rates, port and berth constraints, contract delivery windows and opportunities to capture value through spot-market arbitrage. In addition, sudden geopolitical and maritime disruptions can rapidly alter trade flows and heighten transit risk. Even when the routing problem is simplified to just a few dozen ships, the number of possible decisions becomes exponentially large. That is far beyond what anyone could search exhaustively. Because of that, traditional heuristic methods often land on “good enough” solutions that are not truly optimal, which means real opportunities for efficiency gains are still possible. Downstream (refining and retail) Downstream operations combine complex chemical processing with high-volume, low- margin retail logistics. The priority is improving process efficiency, designing and optimizing molecules and formulations, and using customer analytics to sharpen pricing, demand forecasting and sales performance. Refinery optimization (blending and scheduling) Refineries have to coordinate crude unloading, tank storage and blending so they can hit tight product specs, such as octane and sulphur limits, while protecting margins. It is essentially a resource- constrained scheduling problem. Retail and trading On the retail side, the business focuses on keeping customers from leaving and refining trading strategies to perform well in volatile markets. Downstream benefits most from quantum simulation of chemistry and materials. Accurate quantum models of catalysts, adsorption and reaction pathways could reduce costly trial and error in catalyst selection and process tuning, while supporting the design of cleaner fuels and additives that meet increasingly tight specifications. In parallel, quantum optimization can be applied to refinery blending and scheduling, and emerging quantum machine-learning techniques may complement classical models for demand forecasting and trading analytics where uncertainty and non-linear interactions dominate. Community insights for quantum solutions in fossil fuels In fossil fuels, securing SCADA systems and pipelines was seen as the most promising near-term quantum application, highlighting a strong emphasis on cyber resilience. Other promising areas include surface mapping, pipeline leak detection and methane monitoring, demonstrating quantum’s potential to boost efficiency and performance. Top near-term quantum solutions in fossil fuels FIGURE 5 77% 73% 62% 58% 50%Quantum communication – secure SCADA systems and pipelines from cyberattacks Quantum sensing – subsurface mapping for hydrocarbon reservoirs Quantum sensing – leak detection in pipelines and methane monitoring Quantum computing – reservoir modelling & seismic imaging with higher accuracy Quantum computing – optimize refinery processes to reduce energy intensityQuantum communication – secure SCADA systems and pipelines from cyberattacksFossil fuels (oil, gas and coal) Even small percentage improvements can translate into meaningful fuel savings and higher asset utilization at scale. Source: Community survey, World Economic Forum’s Quantum for Energy and Utilities Working Group, February 2026. Quantum for Energy and Utilities: Key Opportunities for Energy Transition 12