<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Production Systems | Juan Cruz-Benito</title><link>https://juancb.es/tag/production-systems/</link><atom:link href="https://juancb.es/tag/production-systems/index.xml" rel="self" type="application/rss+xml"/><description>Production Systems</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><copyright>©</copyright><lastBuildDate>Mon, 01 Jun 2020 00:00:00 +0000</lastBuildDate><image><url>https://juancb.es/media/sharing.jpg</url><title>Production Systems</title><link>https://juancb.es/tag/production-systems/</link></image><item><title>IBM Quantum Job Scheduling</title><link>https://juancb.es/projects/ibm-quantum-job-scheduling/</link><pubDate>Mon, 01 Jun 2020 00:00:00 +0000</pubDate><guid>https://juancb.es/projects/ibm-quantum-job-scheduling/</guid><description>&lt;p&gt;Designed and implemented the &lt;strong&gt;fair-share queue&lt;/strong&gt; that decides the order in which jobs run on IBM Quantum hardware. It balances access across users and organizations according to their allocations, and it schedules &lt;strong&gt;every job run on every IBM Quantum QPU&lt;/strong&gt; — still in production today.&lt;/p&gt;
&lt;p&gt;Later, as Data &amp;amp; Intelligence Chapter Lead, I led work on &lt;strong&gt;predicting QPU processing time&lt;/strong&gt; for user jobs, which has been deployed on production since 2023. Based on this experience, we published a paper at IEEE QCE 2025 in which we used a dataset of ~150,000 jobs following the IBM Quantum schema, gradient-boosting models (LightGBM) forecast how long each job will take on the hardware, giving scheduling and resource management a better signal than static estimates.&lt;/p&gt;</description></item></channel></rss>