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在谷歌标记比特币和以太坊的量子风险后,Algorand 股价暴涨 50%

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在最近的一篇谷歌量子人工智能论文强调区块链是后量子安全的活生生的例子之后,Algorand 成为加密货币市场最新量子安全辩论中的早期佼佼者。这一消息令市场参与者感到不安,空头希望压低价格,而多头则试图捍卫关键支撑位。观察 $BTC $ETH $ALGO 的反应 - 高于或低于关键水平的决定性走势将确认下一个趋势。

Algorand has emerged as an early standout in the crypto market’s latest quantum security debate after a recent Google Quantum AI paper highlighted the blockchain as a live example of post-quantum cryptography being deployed on a network. The attention came as the paper sharpened concerns around Bitcoin and Ethereum , two networks whose size, age, and design choices could make any future migration to quantum-resistant infrastructure slower and more complicated. Against that backdrop, Algorand’s quieter work on Falcon digital signatures, state proofs, and key rotation suddenly looked less like a niche technical experiment and more like a practical head start. The shift in attention helped lift Algorand’s token sharply over the past week, with traders treating the Google paper as validation of work already underway on the network. According to CryptoSlate's data, ALGO, the blockchain network's native token, is one of the top performers over the past week, gaining around 50% to rise to $0.12 as of press time. Notably, the price performance came less than a week after the token fell to an all-time low of $0.08. Algorand's quiet quantum computing lead over Bitcoin and Ethereum Algorand’s advantage over Bitcoin and Ethereum is narrower than the recent enthusiasm suggests, but it is also more concrete than what many larger chains can currently show. In its paper , Google described Algorand as an example of real-world deployment of post-quantum cryptography on an otherwise quantum-vulnerable blockchain . Related Reading Google slashes quantum cracking estimates by 20X creating $600 billion countdown for Bitcoin and Ethereum Google used zero-knowledge proofs to verify quantum attack estimates without exposing the underlying attack circuits. Mar 31, 2026 · Oluwapelumi Adejumo The distinction was important. It did not say Algorand had solved the problem end-to-end, but it did point to a network that had moved from theory into live implementation. Algorand’s core consensus and b

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