{"version":"1.0","type":"rich","provider_name":"techandbusiness.org","provider_url":"https://techandbusiness.org","title":"Preprint cuts encryption depth for shared quantum model training","author_name":"techandbusiness.org · Tech & Business, AI","thumbnail_url":"https://techandbusiness.org/api/og/newswire/IlGP6A9LHI5NiWZ2xvEUOg","thumbnail_width":1200,"thumbnail_height":630,"width":600,"height":400,"html":"<blockquote class=\"tb-newswire-embed\" style=\"max-width:600px;border-left:3px solid #22d3ee;padding:12px 16px;margin:0;font-family:-apple-system,system-ui,sans-serif;background:#09090b;border-radius:0 8px 8px 0;\">\n      <p style=\"margin:0 0 8px;font-size:10px;font-weight:600;letter-spacing:0.1em;color:#71717a;\">techandbusiness.org · Tech & Business, AI</p>\n      <p style=\"margin:0 0 8px;font-size:18px;font-weight:700;line-height:1.3;color:#fff;\"><a href=\"https://techandbusiness.org/newswire/IlGP6A9LHI5NiWZ2xvEUOg\" style=\"color:#fff;text-decoration:none;\">Preprint cuts encryption depth for shared quantum model training</a></p>\n      <p style=\"margin:0;font-size:14px;color:#a1a1aa;line-height:1.5;\">Researchers report a way to train quantum neural networks across separate clients while keeping their updates encrypted, without the costly encryption resets previously associated with rotation weight...</p>\n    </blockquote>"}