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Generalised Krylov complexity

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arxiv 2306.16118 v3 pith:DET5GPLQ submitted 2023-06-28 hep-th

classification hep-th
keywords generalisedcomplexitykrylovgrowthk-complexitylongtimesresults
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In this paper, we studied a set of generalised Krylov complexity for operator growth. We demonstrate their universal features at both initial times and long times using half-analytical technique as well as numerical results. In particular, by using the logarithmic relation to the Krylov entropy, we establish an inequality (\ref{master}) between the variance of the K-complexity and the generalised notions which holds in the long time limit. Extending the result to finite (but long) times, we show that for fast scramblers, the K-complexity constrains the growth of generalised complexity more stringently than the dispersion bound. However, for slow scramblers, the growth rate of K-complexity is tighter bounded by the generalised complexity in the other way around. Our results enlarge the zoo of Krylov quantities and may shed new light on the future research in this field.

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Cited by 3 Pith papers

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

  1. Brickwall One-Loop Determinant: Spectral Statistics & Krylov Complexity

    hep-th 2024-12 conditional novelty 6.0 of 10

    In the brickwall model of a BTZ black hole, hand-tuned Gaussian randomness at a stretched horizon reproduces random-matrix-theory spectral statistics and Krylov complexity peaks for scalar and fermionic probes.

  2. Higher-Order Krylov State Complexity in Random Matrix Quenches

    hep-th 2024-12 conditional novelty 5.0 of 10

    Higher-order generalized spread complexities show a more pronounced pre-equilibration peak than standard spread complexity in random matrix quenches, quantifying chaos more sharply up to third order.

  3. Statistics and Complexity of Wavefunction Spreading in Quantum Dynamical Systems

    quant-ph 2024-11 conditional novelty 5.0 of 10

    The moments of the spreading-operator measurement distribution are generalized spread complexities, which for GUE Hamiltonians peak more sharply at higher order and obey a norm bound.

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