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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data re-uploading for a universal quantum classifier · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1907.02085/</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/1907.02085/feed.xml" rel="self" type="application/rss+xml" /><item><title>Data re-uploading for a universal quantum classifier — mentioned by thezeroth</title><link>https://news.ycombinator.com/item?id=21075161</link><guid isPermaLink="false">urn:hackerpapers:mention:1907.02085:21075161</guid><pubDate>Wed, 25 Sep 2019 20:40:43 GMT</pubDate><description>&lt;p&gt;This is actually a very cool demo! I did run some simulations similar to this one, but at some point I stopped trying so many different models for the same problem and started facing more difficult tasks (like multi-class, for example). If it is to anyone&amp;#x27;s interest, the problem with escalation of this model lies on what would be the analogous to &amp;quot;activation functions&amp;quot; in classic neural networks. The cool thing about the sigmoid function is that it&amp;#x27;s monotonous, whereas every-day quantum gates are deeply rooted in periodic functions, so the chances of getting stuck inside a bad local minimum…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1907.02085/"&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>
