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Exploiting the causal tensor network structure of quantum processes to efficiently simulate non-Markovian path integrals

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arxiv 1902.00315 v2 pith:RXW5GL6M submitted 2019-02-01 quant-ph cond-mat.mes-hall

classification quant-phcond-mat.mes-hall
keywords functionalinfluencealgorithmquantumtensorevolutionexploitingnetwork
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In the path integral formulation of the evolution of an open quantum system coupled to a Gaussian, non-interacting environment, the dynamical contribution of the latter is encoded in an object called the influence functional. Here, we relate the influence functional to the process tensor -- a more general representation of a quantum stochastic process -- describing the evolution. We then use this connection to motivate a tensor network algorithm for the simulation of multi-time correlations in open systems, building on recent work where the influence functional is represented in terms of time evolving matrix product operators. By exploiting the symmetries of the influence functional, we are able to use our algorithm to achieve orders-of-magnitude improvement in the efficiency of the resulting numerical simulation. Our improved algorithm is then applied to compute exact phonon emission spectra for the spin-boson model with strong coupling, demonstrating a significant divergence from spectra derived under commonly used assumptions of memorylessness.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Tensor-network decoders for process tensor descriptions of non-Markovian noise

    quant-ph 2024-12 conditional novelty 6.0 of 10

    A tensor-network-based maximum likelihood decoder is constructed for quantum error correction under process-tensor noise, with an MPS approximation demonstrated on the five-qubit and Steane codes.

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