<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Fidelity kernels | Giovanni Scala | Quantum Information</title><link>https://giovanniscala.github.io/tag/fidelity-kernels/</link><atom:link href="https://giovanniscala.github.io/tag/fidelity-kernels/index.xml" rel="self" type="application/rss+xml"/><description>Fidelity kernels</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© 2026 Giovanni Scala</copyright><lastBuildDate>Mon, 14 Sep 2026 00:00:00 +0000</lastBuildDate><image><url>https://giovanniscala.github.io/media/icon_hub9af10373a867da90b39ee3f3796e0ee_26097_512x512_fill_lanczos_center_2.png</url><title>Fidelity kernels</title><link>https://giovanniscala.github.io/tag/fidelity-kernels/</link></image><item><title>Quantum Machine Learning</title><link>https://giovanniscala.github.io/project/quantum-machine-learning/</link><pubDate>Mon, 14 Sep 2026 00:00:00 +0000</pubDate><guid>https://giovanniscala.github.io/project/quantum-machine-learning/</guid><description>&lt;p>This research investigates when quantum-data kernels remain informative with finite measurement budgets, with emphasis on resource scaling, concentration effects, and quantum phase-transition learning.&lt;/p></description></item></channel></rss>