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The AI boom is driving an explosive surge in computational demands and reshaping the landscape of technology, infrastructure, and innovation. One of the biggest barriers to widespread AI deployment today is access to power.
Some estimates suggest AI-driven data centres now consume more electricity than entire nations. The World Economic Forum projects a doubling of energy use by data centres from 2024 to 2027, driven by the energy-intensive nature of AI workloads.
This surge in electricity demand is transforming the utilities industry and redefining how and where data centres are built—power is no longer a given. In the U.S, electricity usage is growing for the first time in over a decade, largely because of data centre consumption. Meanwhile, big tech is even turning to nuclear power to fuel their long-term AI strategy, while data centre builders are searching for land parcels in areas with excess power or resorting to building their own power infrastructure, often relying on natural gas generators.
Quantum computers could be the key to reducing AI’s rising energy consumption, offering a more efficient, scalable solution. Unlike traditional computers that evaluate one possibility at a time, quantum computers are designed to explore complex problem landscapes more efficiently, making them well-suited for tackling certain challenges that can be difficult, time-consuming, or costly for classical systems.
This enables them to potentially provide solutions faster, at higher quality, and with greater efficiency. While AI excels at uncovering patterns and predictions, quantum computing identifies the most efficient solutions, making these two powerful technologies complementary. Quantum computers address problems that AI and classical methods struggle with, such as factoring large numbers and solving hard optimisation challenges like vehicle routing and supply chain structuring.
Here are three ways quantum computing could help mitigate the expected disruptive impact of AI’s rising computational demands:
Quantum computing could be used to identify optimal data centre locations based on power availability or assist utility companies in streamlining grid planning and management to support both consumer and data centre needs. GE Vernova, a global energy company, is using quantum computers today to identify weaknesses in the power grid and optimise responses for potential attacks on the grid. E.ON, a European multinational electric utility company, is now using annealing quantum computing to explore energy grid stability.
Early research shows the potential for quantum computing to reduce the amount of computational power needed to run AI workflows. A breakthrough published in Science demonstrated that our D-Wave quantum computer solved a magnetic materials simulation problem in minutes using just 12 kilowatts of power. This task would have taken one of the world’s most powerful exascale supercomputers, a massively parallel GPU system, nearly one million years to solve, consuming more electricity than the world uses annually.
Applying these quantum computing techniques to blockchain hashing and proof of work could also result in substantial enhancements to security and efficiency, potentially reducing electricity costs by up to a factor of 1,000. Quantum computers are very energy efficient and may soon perform complex computations like those needed for blockchain or AI at a fraction of the power required today. Some of the world’s largest supercomputing facilities are now actively exploring how GPUs and quantum processing units could work together to improve problem solving and reduce energy consumption.
In February, Forschungszentrum Jülich, a leading supercomputing centre in Germany, purchased an annealing quantum computer to integrate with the Jülich UNified Infrastructure for Quantum computing (JUNIQ). This integration is expected to enable JUNIQ to connect to the JUPITER exascale computer, potentially enabling breakthroughs in AI and quantum optimisation. JUPITER is anticipated to surpass one quintillion calculations per second. This will likely be the world’s first pairing of an annealing quantum computer with an exascale supercomputer, providing a unique opportunity to observe the technology’s impact on AI computational challenges.
Early evidence suggests that annealing quantum computers can be integrated into quantum-hybrid AI workflows, which could potentially enhance model efficiency and performance. Japan Tobacco’s (JT) pharmaceutical division recently conducted a project that involved using a quantum-hybrid AI workflow to generate new molecules.
Using this hybrid approach, JT enhanced the quality of its AI drug development processes, demonstrating that the quantum AI workflow generated more valid molecules with better drug-like qualities compared to classical methods alone.TRIUMF, Canada’s particle accelerator centre, recently published a paper in npj quantum information demonstrating the first use of annealing quantum computing and deep generative AI to create novel simulation models for the next big upgrade of CERN’s particle accelerator, the Large Hadron Collider—the world’s largest particle accelerator. Traditional simulations of particle collisions are time-consuming and costly, often running on supercomputers for weeks or months. By merging quantum computing with advanced AI, the team was able to perform complex simulations more quickly, accurately and efficiently.
As AI adoption continues to accelerate, its insatiable demand for computational power is upending industries and straining global power resources. We need a better solution for addressing AI’s power demands than simply adding more GPU clusters or building nuclear power plants.
From optimising energy grids and data centre placement to reducing GPU power consumption and enhancing AI model performance, annealing quantum computing offers a promising path forward. Tools like PyTorch plug-ins are even making it easy for developers to incorporate quantum into AI workflows to explore how the technology could address computational challenges. For business leaders navigating the energy-intensive AI era, adopting annealing quantum computing could unlock transformative efficiencies today and tomorrow.
ABOUT THE AUTHOR
Alan Baratz is the CEO of D-Wave.
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