Quantum Hardware, Quantum Funding

Commerce Dept. funds Atom Computing with up to $100M for quantum development.

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November 14, 2026 by Ivy Delaney

The U.S. Department of Commerce will provide Atom Computing with up to $100 million through the CHIPS and Science Act to advance neutral-atom quantum computing hardware. This research and development funding focuses on a critical challenge for the field: manipulating and validating the performance of tens of thousands of qubits. The investment supports initiatives for semiconductors and technology needed for commercially viable quantum computers, including systems integration.

CHIPS Act Funds Atom Computing’s Neutral-Atom Qubit Development

The funding will focus on scaling quantum hardware to tens of thousands of qubits, with the award finalized by the Department of Commerce. The $100 million research and development award, provided through the CHIPS and Science Act, directly supports initiatives to manipulate and validate the performance of these qubits, a critical step toward practical quantum computation. This investment targets hardware development and systems integration, essential for building commercially viable neutral-atom quantum computers.

The company’s efforts will address challenges in controlling a significantly larger number of qubits than currently demonstrated, pushing the boundaries of quantum system design. This research and development award signifies government backing for neutral-atom quantum computing, a technology that uses individual neutral atoms as qubits, and intends to accelerate the development of utility-scale quantum computers with this support.

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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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