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<!DOCTYPE HTML>
<!--
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<html>
<!-- <link rel="stylesheet" href="https://cdn.rawgit.com/jpswalsh/academicons/master/css/academicons.min.css"> -->
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<title>Matthew Beach</title>
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<h1><a href="index.html">Matthew Beach</a></h1>
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<h2>Matthew Beach</h2>
<p>Quantum Physics + Machine Learning</p>
<br>PhD Candidate</br>
<span>
<strong style="">Perimeter Institute | University of Waterloo</strong>
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<h3 style="margin-bottom: 0.2em;margin-top: 0.2em;"> About Me</h3>
<p>
<span>
<strong style="">Condensed Matter Physics | </strong>
<strong style="">Machine Learning |</strong>
<strong style="">Quantum Information</strong>
</span>
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<p>
I'm a researcher working on using machine learning to solve long-standing problems in quantum
condensed matter physics. </p>
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In my spare time, I enjoy playing racquet sports, performing music, and rock climbing.
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<h3 style="margin-bottom: 0.2em;margin-top: 0.2em;"> Projects</h3>
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Here are a few projects that I've worked on recently,
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<ul class="features">
<li >
<a href="https://physicsml.github.io/blog/ML-in-XY.html">
<h3> Machine Learning Topological Defects</h3>
<span class="image left">
<img src="https://physicsml.github.io/images/above.png" width=90%"></span>
<p align="left">
It took over three decades for physicists to recognize that topological defects are responsible for the superfluid helium transition, but what if there was another way to detect these objects with machine learning?
</p>
</a>
</li>
<li >
<a href="https://arxiv.org/abs/1810.02372">
<h3>Super-Resolving Ising Models</h3>
<span class="image left">
<img src="images/sr.jpg" width=90%></span>
<p align="left">
Image super-resolution with neural networks applied to the Ising model. The paper connects concepts from the renormalization group and machine learning.
</p>
</a>
</li>
<li>
<a href="https://qucumber.readthedocs.io/en/stable/">
<h3>QuCumber: quantum wavefunction reconstruction</h3>
<span class="image left">
<img src="images/qucumber.png" width=90%></span>
<p align="left">
Qucumber is an python library for performing quantum state tomography with neural networks, in particular, using restricted Boltzmann machine.
</p>
</a>
</li>
<li>
<a href="http://inspirehep.net/record/1449100/">
<h3>Entanglement Entropy in Holographic States</h3>
<span class="image left">
<img src="images/ee.png" width=100%></span>
<p align="left">
Calculating entanglement entropy in conformal field theories and gravity duals, with connections to kinetmatic de Sitter space.
</p>
</a>
</li>
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