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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hyper-optimized tensor network contraction · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2002.01935/</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/2002.01935/feed.xml" rel="self" type="application/rss+xml" /><item><title>Hyper-optimized tensor network contraction — mentioned by __dt__</title><link>https://news.ycombinator.com/item?id=30513762</link><guid isPermaLink="false">urn:hackerpapers:mention:2002.01935:30513762</guid><pubDate>Tue, 01 Mar 2022 13:20:01 GMT</pubDate><description>&lt;p&gt;&amp;gt; Forget about performance for a moment, and just write those imbricated loops. Right, if we forget about performance a three-nested-loop GEMM is certainly better than any other implementation. But that&amp;#x27;s completely meaningless and you could find such examples for almost any task. This is almost as saying writing hash functions is easy as long as you forget about collisions and avalanche effects. &amp;gt; When you realize that most deep learning library, &amp;quot;einsum is all you need&amp;quot;, and that einsum is just loops and sum, it kind of screams you are doing it the wrong way when using numerical libraries…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2002.01935/"&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>
