Tensor-network DMRG simulations of SU(2) and small-SU(N) bosonic and supersymmetric matrix models give convergent ground states and entanglement measures, with costs that appear to grow polynomially with the number of matrices.
Scrambling with Matrix Black Holes
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abstract
If black holes are not to be dreaded syncs of information but be fully described by unitary evolution, they must scramble in-falling data and eventually leak it through Hawking radiation. Sekino and Susskind have conjectured that black holes are fast scramblers: they generate entanglement at a remarkably efficient rate, with characteristic time scaling logarithmically with the entropy. In this work, we focus on Matrix theory -- M theory in the light-cone frame -- and directly probe the conjecture. We develop a concrete test bed for quantum gravity using the fermionic variables of Matrix theory and show that the problem becomes that of chains of qubits with an intricate network of interactions. We demonstrate that the black hole system evolves much like a Brownian quantum circuit, with strong indications that it is indeed a fast scrambler. We also analyze the Berenstein-Maldacena-Nastase model and reach the same tentative conclusion.
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Simulating matrix models with tensor networks
Tensor-network DMRG simulations of SU(2) and small-SU(N) bosonic and supersymmetric matrix models give convergent ground states and entanglement measures, with costs that appear to grow polynomially with the number of matrices.