From 767f84852715a0ccc717fa25e735b05285a6ca08 Mon Sep 17 00:00:00 2001
From: oscars47 <scholinoscar@gmail.com>
Date: Fri, 22 Dec 2023 00:27:35 -0800
Subject: [PATCH] fixed website label

---
 index.html | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)
diff --git a/index.html b/index.html
index 7ef82fe..9eb4256 100644
--- a/index.html
+++ b/index.html
@@ -1,6 +1,7 @@
 <!DOCTYPE html>
 <html>
 <head>
+    <title>Oscar Scholin</title>
     <h1>&nbsp;&nbsp;Oscar Scholin</h1>
     <style>
 
@@ -166,7 +167,7 @@ <h1>&nbsp;&nbsp;Oscar Scholin</h1>
                     <img src="images/comp.png" alt="results of neural networks." class="project-image">
                     <figcaption class="project-figcaption">Performance comparison as a function of concurrence (the amount of entanglement required to call a state entangled) of different adaptive witnessing strategies, including an analytical method (Population), two XGBoost models, and a variety of neural networks.</figcaption>
                 </figure>
-                <p class="project-description">Wrote a lot of custom code to generate, manipulate, and measure (theoretically and experimentally) 2-qubit quantum states. Trained a variety of machine learning models (eXtreme gradient boosting, neural networks) on 4 million generated states with the goal of predicting the optimal set of next measurements to take in order to most efficiently verify entanglement based on an initial set of projective probabilities, using entanglement witnesses building on those by Riccardi et al. (2019) and previous work by the group. Achieved <a href="https://github.com/Lynn-Quantum-Optics/Summer-2023/blob/main/oscar/writing/oscar_writeup.pdf">  4% increase in performance from previous models</a> and successfully applied the models to experimental data.
+                <p class="project-description">Wrote a lot of custom code to generate, manipulate, and measure (theoretically and experimentally) 2-qubit quantum states. Trained a variety of machine learning models (eXtreme gradient boosting, neural networks) on 4 million generated states with the goal of predicting the optimal set of next measurements to take in order to most efficiently verify entanglement based on an initial set of projective probabilities, using entanglement witnesses building on those by Riccardi et al. (2019) and previous work by the group. Achieved <a href="https://github.com/Lynn-Quantum-Optics/Summer-2023/blob/main/oscar/writing/oscar_writeup.pdf">  4% increase in performance from previous models</a> and successfully applied the mode   ls to experimental data.
 
                     Presented <a href = "https://github.com/Lynn-Quantum-Optics/Summer-2023/blob/main/Witness_Summer_2023_Poster.pdf">  results, "Entanglement Witnessing: a Neural Network Optimization and Experimental Realization", </a> at  <a href="https://physics.unm.edu/SQuInT/2023/index.php">   Southwest Quantum Information and Technology (SQuInT) conference, October 2023.</a> 
 
@@ -199,7 +200,7 @@ <h1>&nbsp;&nbsp;Oscar Scholin</h1>
                         <figcaption class="project-figcaption">Image generated with DALLE-3.</figcaption>
                     </figure>
                     <p class="project-description">
-                        Together with my classmates Larry Liu and Donny Lu, we are working on the a plan to build an optical quantum computer that addresses issues of multi-photon gate complexity and scalibility, with the goal to begin proptyping in Spring 2024. We are working with Professor Gallicchio of Harvey Mudd College as well as Professor Lynn of Harvey Mudd College informally. 
+                        Together with my classmates Larry Liu and Donny Lu, we are working on  a plan to build an optical quantum computer that addresses issues of multi-photon gate complexity and scalibility, with the goal to begin proptyping in Spring 2024. We are working with Professor Gallicchio of Harvey Mudd College as well as Professor Lynn of Harvey Mudd College informally. 
                     </p>
                 </div>
             
