Quantum Security, Quantum Computing News

Benzinga reports Tom Lee warns Bitcoin lags on quantum defense

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August 6, 2026 by Ivy Delaney

Bitcoin faces a potential security threat from quantum computing that could expose private keys in as little as nine minutes, according to Fundstrat co-founder Tom Lee. While networks like Ethereum and Solana are developing quantum resistance, Lee noted that “for Bitcoin they haven’t come to a consensus on how to prevent Q-Day.” A protocol update to shield Bitcoin from these threats could necessitate 305 days of downtime if bandwidth allocation is limited to 25%, even as IBM CEO Arvind Krishna predicts quantum computing will begin to have an impact on revenue by 2028 or 2029.

Bitcoin Vulnerability to Quantum Key Derivation

This speed underscores the immediacy of the threat, even as some industry figures downplay the likelihood of a near-term breach. Jameson Lopp, co-founder and chief security officer at Casa, estimates a “greater than 50% chance” that a truly threatening quantum computer remains a decade away, but the potential for rapid key derivation remains a serious concern. This extended disruption poses a significant problem for a network that prioritizes continuous operation and highlights the practical difficulties of implementing necessary changes.

Lee himself believes quantum computing is “probably not gonna be a problem” for Ethereum and Solana, suggesting different blockchain technologies face varying levels of risk. Coinbase has already formed an advisory board to assess the implications of quantum computing and prepare for potential threats, indicating the industry is taking the possibility seriously despite differing timelines for its realization.

But for bitcoin they haven’t come to a consensus on how to prevent Q-Day,” the Fundstrat co-founder said.

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Bitcoin Cryptography Ethereum IBM Quantum Computing
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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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