$38M CHIPS Funding Proposed for Diraq Silicon Quantum Processors

Diraq is seeking $38 million in CHIPS funding to advance its silicon-based quantum processors, an investment in scalable quantum computing infrastructure. The company has expanded its U. S. operations with a new office in Palo Alto, California, improving access to the semiconductor ecosystem and partnerships with industry leaders including Nvidia, Dell, Global Foundries, and imec. “Silicon Valley established the model for scalable computing,” said Diraq CEO and Founder Andrew Dzurak. “We’re applying that model to quantum computing by building processors that are compatible with CMOS manufacturing and designed for integration into existing compute infrastructure.” This expansion reflects Diraq’s strategy of leveraging established manufacturing processes to build compact, energy-efficient processors capable of scaling to millions of qubits on a single chip.

Diraq is pursuing a strategy that differs from many quantum computing approaches; the company intends to fabricate processors using conventional CMOS manufacturing techniques, which could lower barriers to scalability. This focus on CMOS-native processors is a deliberate attempt to leverage the existing semiconductor infrastructure in Silicon Valley, allowing for the potential integration of millions of qubits onto a single chip. The company’s expansion into Palo Alto, California, is directly linked to this ambition, positioning them closer to key partners and a skilled workforce familiar with these processes. This alignment with existing semiconductor manufacturing addresses a critical bottleneck in quantum computing development, unlike approaches requiring exotic materials or entirely new fabrication facilities, which often hinder rapid scaling and widespread adoption.

The company anticipates doubling its Palo Alto team by year’s end, demonstrating confidence in its technology and the demand for scalable quantum infrastructure. Beyond internal development, Diraq is actively building an ecosystem of collaborators to accelerate progress, including industry giants Nvidia, Dell, Global Foundries, and imec, which demonstrates validation from established players in the computing and semiconductor sectors. Funding from the U. S. Department of Commerce represents a significant financial commitment and underscores the government’s recognition of the potential of silicon-based quantum computing.

Andre Saraiva, Head of Product Development, emphasized the strategic importance of the Palo Alto location, stating, “Establishing a Diraq office in Palo Alto puts us closer to the customers, partners, and talent shaping the future of computing.” Diraq’s success in reaching Stage B of DARPA’s Quantum Benchmarking Initiative, one of only eleven companies globally to do so, further validates its approach and its potential to deliver practical quantum solutions.

We’re applying that same model to quantum computing by building processors that are CMOS-native and designed for integration into existing compute infrastructure. That’s what will enable us to scale to millions of qubits on a single chip and move toward real commercial deployment.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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