aiqbench analysis
This study demonstrates how AI agents can significantly optimize quantum circuits for elliptic-curve point addition, reducing estimated resource costs by 86% for potential attacks on blockchain encryption. While this does not constitute an immediate threat to current networks, it provides critical data for estimating the hardware requirements of future fault-tolerant quantum computers running Shor's algorithm. The findings underscore the accelerating pace of cryptanalysis research and highlight the urgent need for cryptographic migration strategies in the blockchain sector.
Researchers And AI Agents Cut Estimated Quantum Cost of Attacking Bitcoin Encryption by 86%

Insider Brief An open research project combining human scientists with AI agents has sharply reduced the estimated quantum computing resources needed for a key step in attacking the encryption used by Bitcoin, Ethereum and other blockchain networks. The study, posted on arXiv, describes how more than 100 contributors improved a quantum circuit for elliptic-curve point […]
An open research project using human scientists and AI agents cut the estimated quantum resources for a key step in attacking blockchain encryption by 86.1%. The best circuit at the study’s cutoff used 1,151 logical qubits and about 1.3 million Toffoli gates for elliptic-curve point addition. The results do not demonstrate a working attack and exclude several costs required to run Shor’s algorithm on future fault-tolerant quantum hardware. An open research project combining human scientists with AI agents has sharply reduced the estimated quantum computing resources needed for a key step in attacking the encryption used by Bitcoin, Ethereum and other blockchain networks. The study, posted on arXiv, describes how more than 100 contributors improved a quantum circuit for elliptic-curve point addition, one of the main arithmetic operations required to run Shor’s algorithm against elliptic-curve cryptography. The researchers reduced their principal measure of the circuit’s resource cost by 86.1% from the project’s starting point. The team added that this result — with qualifications — is about…
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