Chad Rigetti's New Startup Sygaldry Aims to Solve AI’s Energy and Cost Crisis

Chad Rigetti, the physicist and entrepreneur behind publicly-traded Rigetti Computing, has embarked on a new quantum journey with the launch of Sygaldry, a Y Combinator-backed startup focused on quantum-accelerated AI. Rigetti, who founded his namesake company in 2013 and took it public on NASDAQ in 2022, represents one of the few quantum founders to have built a listed company bearing his name before starting fresh with this new venture.

What sets Sygaldry apart from other quantum computing companies, according to Rigetti, is timing. In a recent Bloomberg Technology interview, he explained that most quantum competitors formed their business roadmaps years before AI became widely adopted, creating organizational inertia that makes it challenging to pivot toward AI applications. Sygaldry was designed from the ground up with AI as the primary focus, positioning it to better capitalize on the intersection of quantum computing and artificial intelligence.

The company’s mission centers on building quantum-accelerated AI servers that combine multiple qubit types within a single, fault-tolerant architecture. This approach leverages the strengths of different quantum technologies while avoiding their individual limitations—similar to how classical computers integrate distinct technologies like RAM, storage, and processors. Sygaldry aims to provide exponential speedups for AI training and inference at a fraction of the cost and energy consumption of traditional GPU-based infrastructure.

Sygaldry Technologies, founded by Chad Rigetti and Idalia Friedson, is developing quantum-accelerated AI servers designed to address the escalating computational cost and energy demands of artificial intelligence. The company aims to expedite both the training and inference phases of AI models by integrating quantum processors as an augmentation to existing classical infrastructure. Sygaldry’s approach centres on a heterogeneous quantum architecture, combining multiple qubit types within a single system to optimise performance and mitigate individual qubit limitations, a strategy analogous to the design of conventional computers. This technology intends to enable faster model development, improved efficiency, and increased accessibility to advanced AI capabilities.

Sygaldry Aims:

• Reduce the time between model releases

• Fine-tune existing models faster

• Enable faster token generation for distributed LLMs

• Unlock faster inference for diffusion models

• Conduct greater exploration in novel model architecture

• Leverage secure quantum communication channels

• Increase AI affordability, accessibility, and personalization

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