Quantum computing researchers are proposing a hybrid quantum-classical approach to ease the energy crisis facing AI data centers, according to a report published by InformationWeek. The concept rests on shifting the most energy-intensive calculations to quantum processors that consume far less power during computation. But the report reveals a critical challenge: while the processors themselves use minimal electricity, the refrigeration systems needed to cool quantum hardware to absolute zero can consume significantly more energy than the computation itself, potentially negating the efficiency gains.
The scale of AI's energy challenge is already mounting. The International Energy Agency projects that global data-center electricity consumption could nearly double by 2030 to around 945 terawatt-hours, slightly exceeding Japan's entire current electricity usage, according to the report. AI-optimized data centers specifically are expected to see their electricity demand more than quadruple by 2030. In the United States, the Department of Energy estimates that data centers could account for up to 12% of total U.S. electricity consumption by 2028. Meanwhile, a regional U.S. grid operator recently warned it might need to order rolling blackouts across 14 states due to electricity shortfalls driven by record demand.
The report explains that quantum's energy advantage stems from two factors: reducing the time required to complete complex calculations and lowering the power needed to perform them. "Quantum computers can manage incredibly complex variables compared to classical systems, while requiring significantly less power," Pranav Gokhale, CTO and co-founder of Infleqtion, told the publication. However, modeling by the National Renewable Energy Lab found that cooling energy in quantum data centers consumes far more than computation itself, according to the report. Arif Gasilov, a partner at Gasilov Group, noted that while "the processor itself barely sips power," the surrounding systems do not, causing costs to "even out in a way."
The report frames quantum computing as a specialized tool rather than a wholesale replacement for classical systems, drawing parallels to how GPUs evolved from niche accelerators to foundational infrastructure over the past 15 years. Alternative quantum techniques using laser control systems at room temperature rather than extreme cooling exist, with energy usage comparable to or less than modern high-performance computer racks, according to Scott Buchholz, quantum computing leader at Deloitte. The gap between rising demand and constrained grid capacity creates direct financial consequences for IT budgets, exposing organizations to volatility in global energy prices, the report finds. CIOs should incorporate energy procurement risk into continuity planning the same way they model supply chain disruption, Gasilov advised. Meanwhile, National Grid Partners is investing in real-time grid monitoring to unlock existing capacity, flexible data center designs that can adjust power demand, and next-generation electrical conductors to accelerate grid upgrades, according to Pradeep Tagare, the firm's head of investments. One dissenting voice in the report argues that the real problem isn't insufficient power but how utilities allocate grid capacity, with hyperscalers "over-filing" permits across multiple jurisdictions because the planning process is too slow and opaque. As AI workloads continue pushing against the limits of traditional computing, quantum offers a favorable path forward for specific computational tasks, though it won't solve every energy challenge confronting AI data centers today, Gokhale concluded. Organizations that treat energy constraints as purely an infrastructure problem risk missing opportunities to rethink which workloads genuinely require massive compute and which can be handled more efficiently through architectural choices.