A tensor-network method enables simulations of inhomogeneous many-body neutrino flavor instabilities, showing earlier equilibration than mean-field approximations with differences arising from initial configurations and boundaries.
Schollwöck, The density-matrix renormalization group in the age of matrix product states, Annals of physics326, 96 (2011)
2 Pith papers cite this work. Polarity classification is still indexing.
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QCNNs are classically simulable via Pauli shadows on low-bodyness subspaces of locally-easy datasets, with explicit simulation demonstrated up to 1024 qubits for phases of matter classification.
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Two-beam Multiparticle Many-body simulations of Inhomogeneous FFI
A tensor-network method enables simulations of inhomogeneous many-body neutrino flavor instabilities, showing earlier equilibration than mean-field approximations with differences arising from initial configurations and boundaries.
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Quantum Convolutional Neural Networks are Effectively Classically Simulable
QCNNs are classically simulable via Pauli shadows on low-bodyness subspaces of locally-easy datasets, with explicit simulation demonstrated up to 1024 qubits for phases of matter classification.