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Quantum Computing Business News, Quantum Companies

SEEQC Files S-1 for Proposed Nasdaq Public Offering

Avatar of Ivy Delaney
June 30, 2026 by Ivy Delaney
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SEEQC has publicly filed a registration statement on Form S-1 with the U.S. Securities and Exchange Commission, signaling its intent to offer shares in an initial public offering. The quantum computing infrastructure company plans to list its common stock on the Nasdaq Global Market under the ticker symbol “SEQC”, positioning it as a relatively early-stage firm entering the public market. Cantor and BTIG are acting as lead book-running managers for the proposed offering, suggesting the investor profile SEEQC is targeting. The company develops and manufactures digital technologies designed to make quantum computing scalable, and its platform demonstrated gate fidelities exceeding 99.9% at nanowatt-scale power consumption, according to the filing.

SEEQC Announces Proposed Nasdaq IPO Filing

SEEQC’s digital quantum control technology achieves gate fidelities exceeding 99.9%. The Securities and Exchange Commission filing signals a relatively early stage of development for a quantum computing infrastructure firm seeking public investment. The company is not yet disclosing the number of shares or price range for the offering, but the filing represents a significant step beyond preliminary discussions and towards becoming a publicly traded entity. These firms’ involvement suggests a focus on institutional investors familiar with high-growth technology ventures. SEEQC’s platform combines superconducting Single Flux Quantum chips with cryogenic CMOS electronics, room-temperature control systems, firmware, and software, aiming to address the challenges of scalability and energy efficiency in quantum computing. The company highlights its work with leading organizations, including NVIDIA and IBM, as well as collaborations with major government research institutions, demonstrating a broad network within the quantum ecosystem. More than half of SEEQC’s workforce hold Ph.D. degrees in fields critical to quantum computing, including physics, electrical engineering, materials science, and computer science; this concentration of advanced expertise underscores the company’s commitment to fundamental research and development.

According to the filing, SEEQC is building scalable quantum computers on a chip, a strategy intended to overcome limitations of existing quantum architectures. The proposed offering is subject to market conditions, and SEEQC cautions that there is no guarantee it will be completed, or on the anticipated terms. The company also notes that copies of the preliminary prospectus may be obtained from Cantor and BTIG, providing specific contact information for interested parties.

The Company’s digital quantum control technology, published in Nature Electronics, demonstrated the first integrated qubit control system operating at millikelvin temperatures, achieving gate fidelities exceeding 99.9% at nanowatt-scale power consumption.

SEEQC

Source: https://www.businesswire.com/news/home/20260629077919/en/SEEQC-Files-Registration-Statement-for-Proposed-Initial-Public-Offering

Disclaimer. This article is for informational purposes only and does not constitute investment, financial or professional advice. The quantum technology industry evolves rapidly and information may become outdated. Always conduct your own research and consult qualified advisers before making investment decisions. Investing in quantum computing companies involves significant risk, including the potential loss of your entire investment, and past performance is not indicative of future results.

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Avatar of Ivy Delaney

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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