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.
Measure of the potential valleys of the supermembrane theory
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abstract
We analyse the measure of the regularized matrix model of the supersymmetric potential valleys, $\Omega$, of the Hamiltonian of non zero modes of supermembrane theory. This is the same as the Hamiltonian of the BFSS matrix model. We find sufficient conditions for this measure to be finite, in terms the spacetime dimension. For $SU(2)$ we show that the measure of $\Omega$ is finite for the regularized supermembrane matrix model when the transverse dimensions in the light cone gauge $\mathrm{D}\geq 5$. This covers the important case of seven and eleven dimensional supermembrane theories, and implies the compact embedding of the Sobolev space $H^{1,2}(\Omega)$ onto $L^2(\Omega)$. The latter is a main step towards the confirmation of the existence and uniqueness of ground state solutions of the outer Dirichlet problem for the Hamiltonian of the $SU(N)$ regularized $\mathrm{D}=11$ supermembrane, and might eventually allow patching with the inner solutions.
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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.