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moved ew first
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pokebot4tw committed Dec 6, 2023
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Expand Up @@ -156,7 +156,24 @@ <h1>&nbsp;&nbsp;Oscar Scholin</h1>
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</div>
<div class="projects">
<div class="project">
<div class="project">
<a href="https://github.com/Lynn-Quantum-Optics/Summer-2023/tree/main" class="project-title">Summer 2023, Entanglement Witnessing</a>
<figure class="project-figure">
<img src="images/quantum.jpeg" alt="waveplate setup" class="project-image">
<figcaption class="project-figcaption">UV Half-wave plate, quartz crystal, precompensation crystal, and BBO in Prof. Lynn's Quantum Optics lab.</figcaption>
</figure>
<figure class="project-figure">
<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 measurements to take 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.

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>

Also experimented with an automatic decomposition of a quantum state into Jones matrices via gradient descent, which <a href="https://github.com/Lynn-Quantum-Optics/Summer-2023/blob/main/oscar/writing/instaq.pdf"> achieved up to 99.3% fidelity</a> in our experimental setup. </p>

</div>
<div class="project">
<a href="https://github.com/Lynn-Quantum-Optics/Fall-2023-Spring-2024" class="project-title">Fall 2023, Maximal LELM Distinguishability for d = 6</a>
<figure class="project-figure">
<img src="images/basis.png" alt="LELM image" class="project-image">
Expand Down Expand Up @@ -185,23 +202,7 @@ <h1>&nbsp;&nbsp;Oscar Scholin</h1>
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.
</p>
</div>
<div class="project">
<a href="https://github.com/Lynn-Quantum-Optics/Summer-2023/tree/main" class="project-title">Summer 2023, Entanglement Witnessing</a>
<figure class="project-figure">
<img src="images/quantum.jpeg" alt="waveplate setup" class="project-image">
<figcaption class="project-figcaption">UV Half-wave plate, quartz crystal, precompensation crystal, and BBO in Prof. Lynn's Quantum Optics lab.</figcaption>
</figure>
<figure class="project-figure">
<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 measurements to take 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.

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>

Also experimented with an automatic decomposition of a quantum state into Jones matrices via gradient descent, which <a href="https://github.com/Lynn-Quantum-Optics/Summer-2023/blob/main/oscar/writing/instaq.pdf"> achieved up to 99.3% fidelity</a> in our experimental setup. </p>

</div>

<div class="project">
<a href="https://github.com/oscars47/UCVS" class="project-title">Summer 2022, Fall 2022; Fall 2023-Spring 2024, p-stars </a>
<figure class="project-figure">
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