Scaling Green Quantum Computing and the Search for Environmental Efficiency

 

By Mariana Meneses

Quantum computing could eventually outperform classical computers on some problems while using less energy. But today’s systems remain energy-intensive in different ways, and no real-world application has yet clearly demonstrated a combined computational and energy advantage. So, is “green quantum advantage” a realistic path to sustainability, or is it still a promise waiting to be proven?  

According to the World Economic Forum, quantum computing could help address the growing energy demands of AI and other computationally intensive technologies, however its environmental advantage will depend heavily on which quantum architectures are scaled.  

The WEF argues that quantum algorithms could theoretically solve some complex problems with far less energy than classical computing, but actual power use varies by hardware: superconducting systems require energy-intensive cryogenic cooling, while neutral-atom platforms operate near room temperature and currently use substantially less electricity.  

When the current problem of circuit errors (“decoherence”) is resolved, quantum computing could be applied to reducing global energy use through better optimization of energy delivery and transportation systems, advances in batteries and materials, and potentially more efficient AI workflows. The article argues that governments and industry should therefore weigh energy scalability alongside computational performance when choosing which quantum technologies to fund, develop, and deploy. 

“While current quantum systems are energy-inefficient prototypes, advances in scale, integration, and application targeting may enable a “green quantum advantage” where quantum platforms outperform classical systems in both speed and energy use.” – J. Nirmaladevi (2026).

A recent study of five major quantum-computing platforms adds empirical detail to the debate over whether quantum computing can deliver an energy advantage in practice. Rather than looking only at how much power a machine draws, the researchers measure how much computation it can perform for the energy it consumes. The article entitled “Energy efficiency of quantum computers” is still in preprint phase (i.e., not peer-reviewed) and was uploaded to the preprint server arXiv in May 2026 by an international group of researchers led by Miquel Carrasco-Codina, from the Universitat Politècnica de Catalunya.

 

 

The researchers found that the main sources of energy use differ substantially across architectures. In their reference models, cryogenic cooling dominates superconducting and silicon-spin systems, while the room-temperature trapped-ion system is dominated by HVAC requirements associated with maintaining stable conditions for its laser setup. Neutral-atom computers avoid cryogenic refrigeration of the processor, but their laser-intensive qubit-control hardware accounts for most of the modeled power demand. Photonic systems can manipulate light using linear optics at room temperature, although the architecture examined in the study still requires cryogenic cooling for its quantum-dot photon source and single-photon detectors.  

Avoiding cryogenic refrigeration therefore does not eliminate substantial system-level energy demands. The authors caution, however, that the platforms are at different stages of development and that their estimates should not be used as a direct ranking of their energy efficiency 

For more on photonics in quantum computing, check out TQR’s October 2025 article “With Diamond Film and GKP Qubits, is Light About to Take Centre Stage in Quantum Computing and Drug Discovery Breakthroughs?”. 

Even this operational comparison captures only part of quantum computing’s environmental footprint.

In the article “Sustainable generative AI and quantum computing: review assessment on the environmental impact of generative AI and quantum technologies”, published in the journal Frontiers in Sustainability in 2026, Esther O. Esho, from the Australian New Zealand Society for Ecological Economics, and co-authors broaden the assessment to the full life cycle of quantum technologies.  

Alongside electricity used for cooling and control, the authors highlight the environmental costs of manufacturing specialized hardware, sourcing scarce and potentially harmful materials, and eventually disposing of equipment. They also point to the additional energy demands that quantum error correction could create as systems scale. In this broader framework, a “green quantum advantage” would be reached only when the energy and environmental benefits of using quantum computing for a particular task outweigh the costs of building and operating the system, something that remains uncertain for future fault-tolerant machines.

 

 

The environmental case for quantum computing, however, will depend not only on reducing its own footprint, but also on whether it can help solve environmental problems that are difficult to address with classical computing. 

A 2026 article entitled “Addressing ecological challenges from a quantum computing perspective”, published in Methods in Ecology and Evolution, explores this possibility in ecology. Maxime Clenet, from the Université de Sherbrooke, in Canada, and co-authors argue that quantum algorithms could eventually help researchers analyze ecological problems that become increasingly difficult as datasets, networks and models grow in complexity.  

Potential applications include predicting species distributions, analyzing food webs and other ecological networks, modelling population and ecosystem dynamics, and optimizing conservation strategies. For now, however, most applications remain experimental: current quantum computers are still small, noisy and prone to errors, and ecological data must first be translated into a form a quantum computer can process. The authors note that the conversion itself can be so demanding that it may reduce or even eliminate the theoretical speed advantage. 

Despite their ongoing evolution, quantum computing and other technologies that include AI could significantly accelerate progress across all United Nations Sustainable Development Goals – Tankiso Moloi (2026) 

One route to the goal of making quantum computing more environmentally efficient may be to increase the temperatures at which quantum systems can operate.  

A partner-content article produced by Nature Research Custom Media and Tsinghua University reports that quantum physicist Dongling Deng and his team are developing ways to protect superconducting qubits from thermal excitations, which can disrupt quantum states. The article notes that even raising operating temperatures to around 5 kelvin could allow simpler helium-based refrigeration, reducing system complexity and cost, although room-temperature operation is probably still decades away.  

Deng’s group has explored topological approaches that first used disorder and more recently symmetry to prevent thermal excitations from affecting stored quantum information. For quantum error correction, they employed a process that uses 32 physical qubits to encode four logical qubits more efficiently than the surface code. The article also links higher-temperature quantum computing to AI, suggesting that quantum computers could eventually solve certain AI problems exponentially faster than classical systems and reduce the high energy costs associated with conventional AI 

“If we can solve an AI problem with a quantum computer that cannot be solved with a classical computer, that would be a massive breakthrough” – Dongling Deng, from Tsinghua University, in China. 

 

Room-temperature quantum computer operation does not automatically mean lower energy use, and computational speed gains can be offset by the energy and resource costs of manufacturing, cooling, error correction, and translating problems into quantum-compatible form. Whether a genuine “green quantum advantage” will emerge seems to depend on which hardware architecture gets scaled, whether the full lifecycle footprint (manufacturing, materials, disposal, and error correction overhead) is accounted for rather than just operational electricity, and whether quantum computing can actually deliver practical speedups on real-world problems (like ecological modeling) once the overhead of translating problems into quantum-usable form is factored in.  

Moreover, it may depend on whether the biggest industry players will prioritize environmental efficiency alongside raw computational performance.

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