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<p style="margin-top:0;margin-bottom:0">Hello fellow data scientists!</p>
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<p style="margin-top:0;margin-bottom:0">I recently came across another interesting paper which uses machine learning in physics, so I thought I would forward along a link:</p>
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<p style="margin-top:0;margin-bottom:0"><a href="https://arxiv.org/abs/1606.02318" class="OWAAutoLink" id="LPlnk982057" previewremoved="true">https://arxiv.org/abs/1606.02318</a></p>
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<a id="LPUrlAnchor_15487279173270.3228327614534724" style="text-decoration: none;" href="https://arxiv.org/abs/1606.02318" target="_blank">Solving the Quantum Many-Body Problem with Artificial Neural Networks</a></div>
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arxiv.org</div>
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The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the non-trivial correlations encoded in the exponential complexity of the many-body wave function. Here we demonstrate that systematic machine learning
of the wave function can reduce this complexity to a tractable computational form, for some notable cases of physical interest. We introduce a variational representation of quantum states based on artificial neural networks with variable number of hidden neurons.
A reinforcement-learning scheme is then demonstrated, capable of either finding the ground-state or describing the unitary time evolution of complex interacting quantum systems. We show that this approach achieves very high accuracy in the description of equilibrium
and dynamical properties of prototypical interacting spins models in both one and two dimensions, thus offering a new powerful tool to solve the quantum many-body problem.</div>
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Cheers!
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<p style="margin-top:0;margin-bottom:0">David Livermore</p>
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<p style="margin-top:0;margin-bottom:0">PS If you happen to come across a physics paper which use machine learning, let me know!<br>
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