Twirled Perfect Tensor Networks: Computationally covariant holographic tensor networks
Abstract
We define a novel class of tensor networks motivated by the Python's Lunch Conjecture (PLC) in local tensor network models of the black hole interior. We start from the observation that, for extended black brane states with short-range correlations, the PLC predicts a complexity that is smaller than the upper bound for generic short-range correlated states. We argue that the PLC makes implicit assumptions about the fine structure of the relevant tensor networks modeling gravity that render them non-generic. We demonstrate this explicitly in random tensor network models of the python's lunch, where the exponential complexity is not generally controlled by the PLC exponent. We trace the difference with the PLC to a lack of "computational covariance" in random tensor networks: while the PLC is motivated by an ability to arbitrarily decompose space into low-complexity units provided certain basic rules are followed, we show that random tensor networks do not generically have this property. We propose another class of tensor networks built from what we call "twirled perfect tensors" that do satisfy the computational covariance property and have a complexity bounded by the PLC value. We still find a discrete limitation from local postselection that appears to be absent in gravity. Moreover, we show that this class of tensor networks combines desirable holographic features of perfect tensor networks and random tensor networks, for example, it obeys a lattice Ryu-Takayanagi formula for arbitrary boundary subregions. Though motivated by holography, these tensor networks provide a flexible framework with potential applications beyond quantum gravity.
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BibTeX
@misc{arora2026twirled,
title = {{Twirled Perfect Tensor Networks: Computationally covariant holographic tensor networks}},
author = {Gurbir Arora and Matthew Headrick and Albion Lawrence and Martin Sasieta and Brian Swingle and Connor Wolfe},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2605.23670},
eprint = {2605.23670},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.23670},
}
RIS
TY - UNPB
TI - Twirled Perfect Tensor Networks: Computationally covariant holographic tensor networks
AU - Gurbir Arora
AU - Matthew Headrick
AU - Albion Lawrence
AU - Martin Sasieta
AU - Brian Swingle
AU - Connor Wolfe
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2605.23670
UR - https://arxiv.org/abs/2605.23670
AB - We define a novel class of tensor networks motivated by the Python's Lunch Conjecture (PLC) in local tensor network models of the black hole interior. We start from the observation that, for extended black brane states with short-range correlations, the PLC predicts a complexity that is smaller than the upper bound for generic short-range correlated states. We argue that the PLC makes implicit assumptions about the fine structure of the relevant tensor networks modeling gravity that render them non-generic. We demonstrate this explicitly in random tensor network models of the python's lunch, where the exponential complexity is not generally controlled by the PLC exponent. We trace the difference with the PLC to a lack of "computational covariance" in random tensor networks: while the PLC is motivated by an ability to arbitrarily decompose space into low-complexity units provided certain basic rules are followed, we show that random tensor networks do not generically have this property. We propose another class of tensor networks built from what we call "twirled perfect tensors" that do satisfy the computational covariance property and have a complexity bounded by the PLC value. We still find a discrete limitation from local postselection that appears to be absent in gravity. Moreover, we show that this class of tensor networks combines desirable holographic features of perfect tensor networks and random tensor networks, for example, it obeys a lattice Ryu-Takayanagi formula for arbitrary boundary subregions. Though motivated by holography, these tensor networks provide a flexible framework with potential applications beyond quantum gravity.
ER -