<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Quantum Federated Learning on ระบบอัตโนมัติอัจฉริยะสำหรับแบบสอบถามและการปฏิบัติตาม</title><link>https://blog.procurize.ai/th/tags/quantum-federated-learning/</link><description>Recent content in Quantum Federated Learning on ระบบอัตโนมัติอัจฉริยะสำหรับแบบสอบถามและการปฏิบัติตาม</description><generator>Hugo</generator><language>th</language><atom:link href="https://blog.procurize.ai/th/tags/quantum-federated-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>กราฟความรู้แบบเฟดอเรตควอนตัมสำหรับหลักฐานการปฏิบัติตามหลายกฎระเบียบแบบเรียลไทม์</title><link>https://blog.procurize.ai/th/quantum-federated-knowledge-graph-for-real-time-compliance/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.procurize.ai/th/quantum-federated-knowledge-graph-for-real-time-compliance/</guid><description>&lt;h1 id="กราฟความรแบบเฟดอเรตควอนตมสำหรบหลกฐานการปฏบตตามหลายกฎระเบยบแบบเรยลไทม">กราฟความรู้แบบเฟดอเรตควอนตัมสำหรับหลักฐานการปฏิบัติตามหลายกฎระเบียบแบบเรียลไทม์&lt;/h1>
&lt;p>Enterprises today face a relentless stream of regulatory updates—from &lt;a href="https://gdpr.eu/" target="_blank" rel="noreferrer nofollow">GDPR&lt;/a> and &lt;a href="https://oag.ca.gov/privacy/ccpa" target="_blank" rel="noreferrer nofollow">CCPA&lt;/a> to industry‑specific standards such as &lt;a href="https://www.iso.org/standard/27001" target="_blank" rel="noreferrer nofollow">ISO 27001&lt;/a>, &lt;a href="https://secureframe.com/hub/soc-2/what-is-soc-2" target="_blank" rel="noreferrer nofollow">SOC 2&lt;/a>, and the emerging &lt;a href="https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai" target="_blank" rel="noreferrer nofollow">EU AI Act&lt;/a>. Traditional compliance pipelines rely on batch‑oriented data collection, manual evidence mapping, and periodic audits, which introduce latency, human error, and costly rework.&lt;/p>
&lt;p>A &lt;strong>Quantum Federated Knowledge Graph (QFKG)&lt;/strong> re‑imagines this workflow by fusing three cutting‑edge technologies:&lt;/p>
&lt;ol>
&lt;li>&lt;strong>Quantum‑enhanced federated learning&lt;/strong> – leveraging quantum processors to accelerate model aggregation across distributed data silos without exposing raw data.&lt;/li>
&lt;li>&lt;strong>Self‑evolving knowledge graphs&lt;/strong> – continuously ingesting policy changes, audit logs, and sensor streams to maintain an up‑to‑date semantic representation of compliance artifacts.&lt;/li>
&lt;li>&lt;strong>Zero‑knowledge proof (ZKP) verification&lt;/strong> – providing cryptographic evidence that a claim holds true without revealing the underlying data.&lt;/li>
&lt;/ol>
&lt;p>Together, these components enable &lt;strong>instant, trustworthy compliance evidence&lt;/strong> that can be queried in real time by auditors, risk managers, and automated governance pipelines.&lt;/p></description></item></channel></rss>