I had been vaguely aware that there was an important connection between tensor network representations of quantum many-body states (e.g., matrix product states) and artificial neural nets, but it didn’t really click together until I saw Roger Melko’s nice talk on Friday about his recent paper with Torlai et al.:^{ a }

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In particular, he sketched the essential equivalence between matrix product states (MPS) and restricted Boltzmann machines^{ b } (RBM) before showing how he and collaborators could train an efficient RBM representations of the states of the transverse-field Ising and XXZ models with a small number of local measurements from the true state.

As you’ve heard me belabor ad nauseum, I think identifying and defining branches is the key outstanding task inhibiting progress in resolving the measurement problem. I had already been thinking of branches as a sort of “global” tensor in an MPS, i.e., there would be a single index (bond) that would label the branches and serve to efficiently encode a pure state with long-range entanglement due to the amplification that defines a physical measurement process. (More generally, you can imagine branching events with effects that haven’t propagated outside of some region, such as the light-cone or Lieb-Robinson bound, and you might even make a hand-wavy connection to entanglement renormalization.) But I had little experience with constructing MPSs, and finding efficient representations always seemed like an ad-hoc process yielding non-unique results.… [continue reading]