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Sygaldry Technologies, Inc. announced that it has raised $139M in Series A and Seed financing to build quantum-accelerated AI servers. The $105M Series A in March 2026 was led by Breakthrough Energy Ventures, following a $34M seed round led by Initialized Capital. Sygaldry’s servers aim to exponentially speed up critical AI algorithms, reducing the cost and power needed to train and operate increasingly large models.
“We’re building quantum computers that meet the specific requirements for AI processing, with the goal of enabling a fundamentally more efficient way of converting megawatts into intelligence,” said Sygaldry CEO and co-founder Chad Rigetti.
Demand for more cost- and energy-efficient AI infrastructure is soaring. An estimated $5.2 trillion in capital expenditure is needed by 2030 to meet global demand for AI, including approximately 125 GW of new power generation capacity.
“The AI industry is advancing faster than ever and needs a breakthrough in performance per watt,” said Carmichael Roberts at Breakthrough Energy Ventures. “Sygaldry's vision for bringing quantum directly to the AI data center has the potential to deliver exactly that, bending the cost and energy curve at the moment it matters most."
In addition to Breakthrough Energy Ventures and Initialized Capital, investors include Y Combinator, Rock Yard Ventures, IQT, University of Michigan, QDNL Participations, Expeditions Fund, 468 Capital, Morpheus Ventures, WTI, Overmatch Ventures, RRE Ventures, and Switch Ventures.
Sygaldry servers are designed to address constraints in AI training and inference by operating alongside classical infrastructure within the data center. The company is also developing quantum algorithms that plug into the tools AI researchers already use.
“We are working at the frontier of quantum and AI simultaneously, because we believe their intersection will define the next era of computing,” said co-founder Michael Keiser. “Our technology will accelerate the classical algorithms AI teams already rely on. In parallel, we are developing entirely new quantum-native approaches to AI that classical systems simply cannot match.”
