Quantum hidden Markov models are shown to be identifiable via a finite set of word probabilities, and the minimal quantum memory dimension is proved to be at least the square root of the minimal generalized hidden Markov model dimension.
Hidden Quantum Markov Models and non-adaptive read-out of many-body states
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abstract
Stochastic finite-state generators are compressed descriptions of infinite time series. Alternatively, compressed descriptions are given by quantum finite- state generators [K. Wiesner and J. P. Crutchfield, Physica D 237, 1173 (2008)]. These are based on repeated von Neumann measurements on a quantum dynamical system. Here we generalise the quantum finite-state generators by replacing the von Neumann pro jections by stochastic quantum operations. In this way we assure that any time series with a stochastic compressed description has a compressed quantum description. Moreover, we establish a link between our stochastic generators and the sequential readout of many-body states with translationally-invariant matrix product state representations. As an example, we consider the non-adaptive read-out of 1D cluster states. This is shown to be equivalent to a Hidden Quantum Model with two internal states, providing insight on the inherent complexity of the process. Finally, it is proven by example that the quantum description can have a higher degree of compression than the classical stochastic one.
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Identifiability and minimality bounds of quantum and post-quantum models of classical stochastic processes
Quantum hidden Markov models are shown to be identifiable via a finite set of word probabilities, and the minimal quantum memory dimension is proved to be at least the square root of the minimal generalized hidden Markov model dimension.