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AI agents pose security risk to quantum error correction, study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the security vulnerabilities of quantum error correction systems when influenced by AI agents. The study identifies an ambiguity in syndrome records that can lead to incorrect recovery selections, potentially allowing an attacker to introduce harmful updates. Researchers propose using calibration measurements to provide missing sign information and ensure certified recovery updates, even with uncertainty or drift. Experiments on toric and surface codes demonstrate that calibration-confidence checks can reject malicious proposals while still accepting beneficial ones, thus enhancing the security of quantum error correction against AI-driven attacks. AI

IMPACT Highlights potential security risks of AI in critical infrastructure like quantum computing, necessitating robust validation mechanisms.

RANK_REASON Academic paper on AI security in quantum computing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI agents pose security risk to quantum error correction, study finds

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Academic paper on AI security in quantum computing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · A. Bar{\i}\c{s} \"Ozg\"uler ·

    Securing quantum error correction against misleading advice from AI agents

    arXiv:2609.19090v1 Announce Type: cross Abstract: Can an attacker turn influence over an artificial intelligence (AI) adviser into a harmful quantum error-correction update? We identify an ambiguity in passive syndrome records that obstructs recovery selection, then show how addi…

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