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The Post-Quantum Transition Can’t Be Postponed Any Longer
Google's research shows quantum computers could crack Bitcoin keys in minutes, urging a shift to quantum-resistant cryptography by 2029 to protect one-third of exposed Bitcoin wallets.
- On Monday, Google's Quantum AI team published research indicating that quantum computers could break the 256-bit encryption protecting Bitcoin and Ethereum wallets using fewer than 500,000 physical qubits, roughly a 20-fold reduction from previous estimates.
- The research references Shor's algorithm, which could derive private keys from exposed public keys; roughly 6.9 million Bitcoin—about one-third of total supply—currently sit in vulnerable wallet addresses.
- Google has set a 2029 target to transition its own systems to post-quantum cryptography. Ethereum Foundation researcher Justin Drake noted the optimized quantum circuit is "just 100 million Toffoli gates," creating a "surprisingly shallow" challenge.
- Bitcoin's decentralized nature makes upgrading difficult, requiring broad consensus. Binance co-founder Changpeng Zhao urged calm, noting crypto can upgrade to quantum-resistant algorithms, while developers test BIP 360 to introduce quantum-resistant wallet formats.
- Experts warn Bitcoin needs to address these vulnerabilities in the next three years. Although full migration could take up to 10 years, the research suggests the quantum threat is closer than previously modeled.
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26 Articles
Alphabet Climbs 5%: Google's Nine-Minute Bitcoin Warning Is Turning Heads on Wall Street
Quick Read Alphabet (GOOGL) stock spiked to $287 on Tuesday, bouncing from a 12.56% YTD decline, as Google’s quantum computing team claims its systems could crack Bitcoin (BTC) encryption in nine minutes. Recent developments suggest that Alphabet leads the quantum race, with profound implications for cybersecurity and AI infrastructure. Alphabet’s fundamentals support the bounce: Q4 2025 EPS beat estimates ($2.82 vs. $2.63), revenue topped ex…
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Total News Sources26
Leaning Left3Leaning Right2Center2Last UpdatedBias Distribution43% Left
Bias Distribution
- 43% of the sources lean Left
43% Left
L 43%
C 29%
R 28%
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