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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Perfect Sampling with Unitary Tensor Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1201.3974/</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/1201.3974/feed.xml" rel="self" type="application/rss+xml" /><item><title>Perfect Sampling with Unitary Tensor Networks — mentioned by miles7</title><link>https://news.ycombinator.com/item?id=35270098</link><guid isPermaLink="false">urn:hackerpapers:mention:1201.3974:35270098</guid><pubDate>Thu, 23 Mar 2023 03:11:39 GMT</pubDate><description>&lt;p&gt;As one of the authors, I&amp;#x27;m ok with reasonable disagreements (e.g. see D. Bacon&amp;#x27;s below) about whether our overall conclusions are justified. But I feel it important to point out that your comment above is just a pure misrepresentation of the procedure in our paper. In a future draft we will bring out this point more clearly, but we do not unroll the post-oracle state into a vector of size 2^n and just look at the winners. We use a well-established sampling technique from the matrix product state literature (reference: https://arxiv.org/abs/1201.3974, also cited in the paper) which runs…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1201.3974/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cond-mat.str-el</category></item></channel></rss>
