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2nd meetup: add first abstract
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Expand Up @@ -32,14 +32,23 @@ <h3> Next meetup </h3>
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<tr><th><b>03.12.2019</br></th>
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<b>Location:</b> <a href=https://quantstack.net/index.html>Artelys</a> (81 rue Saint Lazare, 75009, Paris)</br>
<b>Location:</b> <a href="https://www.artelys.com">Artelys</a> (81 rue Saint Lazare, 75009, Paris)</br>
<i>
Please fill-in <a href="https://docs.google.com/forms/d/e/1FAIpQLSeHt3aB81E8qAFVuR6wo8YejLSPjGjW_q_qWveQ9-d5xom0SQ/viewform?vc=0&c=0&w=1">this form</a> to register to the event.
</i></br>
<b>Schedule:</b> 19h </br>
<b>Topics:</b>
<p>
<a href="https://www.mpq.univ-paris-diderot.fr/?Nouvelle-traduction-1510-Filippo-Vicentini">Filippo</a> will give a talk.
<a href="https://www.mpq.univ-paris-diderot.fr/?Filippo-Vicentini">Filippo</a> will give a talk about
<i>"NeuralQuantum.jl: Approaching Quantum Physics with Neural Networks".</i>
<a href="https://github.com/PhilipVinc/NeuralQuantum.jl">[Github]</a>
<div class="summary">
The state of an open quantum system is completely determined by its density matrix, which evolves according to a specific equation called the Lindblad master equation.
When the system is composed by many interacting particles, the exponential complexity arising from the many-body problem makes it impossible to simulate.
We recently developed a novel approach to the problem, combining the representative power of neural networks with approximate optimization schemes based on Markov-chain sampling.
Unfortunately, the algorithm that emerged is somewhat different from traditional machine-learning problems, and it has been hard to exploit existing frameworks.
During this talk we will detail some key concepts of this novel research direction and how Julia dispatch system empowered us to rapidly iterate on several prototypes during our research.
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