Machine learning in physics

<p>Applying <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a> (ML) (including <a href="/facts/Deep_learning/JLuwD3ea">deep learning</a>) methods to the study of quantum systems is an emergent area of physics research. A basic example of this is <a href="/facts/Quantum_tomography/RGTYGU8X">quantum state tomography</a>, where a quantum state is learned from measurement. Other examples include learning Hamiltonians, learning quantum <a href="/facts/Phase_transition/eLP2BY8R">phase transitions</a>, and automatically generating new quantum experiments. ML is effective at processing large amounts of experimental or calculated data in order to characterize an unknown quantum system, making its application useful in contexts including <a href="/facts/Quantum_information/Bl9QIvHj">quantum information theory</a>, quantum technology development, and computational materials design. In this context, for example, it can be used as a tool to interpolate pre-calculated <a href="/facts/Machine-learned_interatomic_potential/yBXHyLb7">interatomic potentials</a>, or directly solving the <a href="/facts/Schr%25C3%25B6dinger_equation/3csgSIWF">Schrödinger equation</a> with a <a href="/facts/Variational_method_(quantum_mechanics)/vGHjImQT">variational method</a>.
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Machine learning in physics open-in-new

Machine learning in physics