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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>$2^{1296}$ Exponentially Complex Quantum Many-Body Simulation via Scalable Deep Learning Method · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2204.07816/</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/2204.07816/feed.xml" rel="self" type="application/rss+xml" /><item><title>$2^{1296}$ Exponentially Complex Quantum Many-Body Simulation via Scalable Deep Learning Method — mentioned by inasio</title><link>https://news.ycombinator.com/item?id=32003469</link><guid isPermaLink="false">urn:hackerpapers:mention:2204.07816:32003469</guid><pubDate>Wed, 06 Jul 2022 16:43:28 GMT</pubDate><description>&lt;p&gt;There&amp;#x27;s a bit of drama in that there are unofficial reports of two systems in China with higher performance [0], the arXiv paper listed below talks about a 40 million core system with around double theoretical performance than Frontier, and there&amp;#x27;s apparently a second system online with similar performance. I personally suspect that they didn&amp;#x27;t submit benchmarks to the top500 simply because those don&amp;#x27;t run well enough in the systems [0] https://arxiv.org/pdf/2204.07816.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2204.07816/"&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>
