.@physicstoday 'It sounds like science fiction, but the fusion (between quantum physics and machine learning) is happening right now.' @carrasqu * @rgmelko used Monte Carlo-sampled equilibrium spin configurations of an Ising Model to train a feedforward neural network https://t.co/qctnlEvF7Z with the Adaptive Moments (Adam) algorithm (using a cross-entropy loss function w L2 regularization) to identify paramagnetic/ ferromagnetic states; after training, network correctly classified samples not seen before. +, it located Tc & found the critical scaling exponents! pic.twitter.com/dWOPgo0NTh
— Satyen Baindur (@Satyen_Baindur) March 2, 2019
Figure above shows critical point Tc for the triangular lattice Ising model (4m C&M @arxiv 1605.01735v1). PT article also discusses work of @gppcarleo& @matthiastroyer on Restricted #BoltzmannMachines & quantum states in detail; + has nice intro to #TensorNetwork Representations.— Satyen Baindur (@Satyen_Baindur) March 2, 2019
