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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>The Physics of Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2209.11954/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2209.11954/feed.xml" rel="self" type="application/rss+xml" /><item><title>The Physics of Learning — mentioned by fermionik</title><link>https://news.ycombinator.com/item?id=39729757</link><guid isPermaLink="false">urn:hackerpapers:mention:2209.11954:39729757</guid><pubDate>Sat, 16 Mar 2024 21:44:53 GMT</pubDate><description>&lt;p&gt;Physical learning machines require noise to learn. They are also necessarily dissipative. See https://arxiv.org/abs/2209.11954. The key is to engineer the noise to maximise the learning rate. In classical devices, stochastic switching is controlled by temperature through the Kramers rate. This means kT controls energy loss. If you use dissipative quantum tunnelling this is not the true thermodynamic lower bound. Any quantum nonlinear dissipative system, with a far from equilibrium steady state, is a good case to consider. Dispersive optical bistability, realised in SC quantum circuits, is the…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2209.11954/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>quant-ph</category></item></channel></rss>
