Towards a Refinement of Krylov Complexity: Scrambling, Classical Operator Growth and Replicas
Hugo A. Camargo, Yichao Fu, Keun-Young Kim, and Yeong Han Park

TL;DR
This paper introduces logarithmic Krylov complexity, a new operator growth measure that effectively distinguishes genuine scrambling from saddle effects in quantum systems, and extends the concept to classical dynamics.
Contribution
It proposes and tests a replica-based logarithmic Krylov complexity that refines operator growth analysis, applicable to both quantum and classical systems, capturing integrability and scrambling.
Findings
logK complexity discriminates genuine from saddle-dominated scrambling in quantum models
Refined logK complexity captures integrable properties in certain models
Classical versions of operator growth measures show no false positives from unstable saddles
Abstract
We propose and test logarithmic Krylov (logK) complexity, an operator growth measure akin to Krylov complexity defined through a replica approach, as a viable probe of early-time operator scrambling without false positives. In finite-dimensional quantum systems, such as the Lipkin--Meshkov--Glick (LMG) model and the mixed-field Ising model at the chaotic point, we provide numerical evidence that logK-complexity discriminates between genuine and saddle-dominated scrambling at early times, correctly avoiding the exponential contribution coming from the unstable saddle in the former case, and closely tracking the conventional Krylov complexity in the latter. In integrable quantum systems admitting infinite-dimensional Krylov subspaces, such as the SYK model and the quantum inverted harmonic oscillator, we show that by modifying the Krylov spreading operator, obtained through…
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