Bending the Bruhat-Tits Tree I:Tensor Network and Emergent Einstein
Equations
- URL: http://arxiv.org/abs/2102.12023v3
- Date: Thu, 20 May 2021 06:31:12 GMT
- Title: Bending the Bruhat-Tits Tree I:Tensor Network and Emergent Einstein
Equations
- Authors: Lin Chen, Xirong Liu and Ling-Yan Hung
- Abstract summary: We show how a p-adic CFT encodes geometric information of a dual geometry even as we deform the CFT away from the fixed point.
This is perhaps a first quantitative demonstration that a concrete Einstein equation can be extracted directly from the tensor network.
- Score: 6.127256542161883
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As an extended companion paper to [1], we elaborate in detail how the tensor
network construction of a p-adic CFT encodes geometric information of a dual
geometry even as we deform the CFT away from the fixed point by finding a way
to assign distances to the tensor network. In fact we demonstrate that a unique
(up to normalizations) emergent graph Einstein equation is satisfied by the
geometric data encoded in the tensor network, and the graph Einstein tensor
automatically recovers the known proposal in the mathematics literature, at
least perturbatively order by order in the deformation away from the pure
Bruhat-Tits Tree geometry dual to pure CFTs. Once the dust settles, it becomes
apparent that the assigned distance indeed corresponds to some Fisher metric
between quantum states encoding expectation values of bulk fields in one higher
dimension. This is perhaps a first quantitative demonstration that a concrete
Einstein equation can be extracted directly from the tensor network, albeit in
the simplified setting of the p-adic AdS/CFT.
Related papers
- Geometric monotones of violations of quantum realism [89.99666725996975]
Quantum realism states that projective measurements in quantum systems establish the reality of physical properties, even in the absence of a revealed outcome.
This framework provides a nuanced perspective on the distinction between classical and quantum notions of realism, emphasizing the contextuality and complementarity inherent to quantum systems.
We derive geometric monotones of quantum realism using trace distance, Hilbert-Schmidt distance, Schatten $p$-distances, Bures, and Hellinger distances.
arXiv Detail & Related papers (2024-12-16T10:22:28Z) - Tensor cumulants for statistical inference on invariant distributions [49.80012009682584]
We show that PCA becomes computationally hard at a critical value of the signal's magnitude.
We define a new set of objects, which provide an explicit, near-orthogonal basis for invariants of a given degree.
It also lets us analyze a new problem of distinguishing between different ensembles.
arXiv Detail & Related papers (2024-04-29T14:33:24Z) - Transolver: A Fast Transformer Solver for PDEs on General Geometries [66.82060415622871]
We present Transolver, which learns intrinsic physical states hidden behind discretized geometries.
By calculating attention to physics-aware tokens encoded from slices, Transovler can effectively capture intricate physical correlations.
Transolver achieves consistent state-of-the-art with 22% relative gain across six standard benchmarks and also excels in large-scale industrial simulations.
arXiv Detail & Related papers (2024-02-04T06:37:38Z) - A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems [87.30652640973317]
Recent advances in computational modelling of atomic systems represent them as geometric graphs with atoms embedded as nodes in 3D Euclidean space.
Geometric Graph Neural Networks have emerged as the preferred machine learning architecture powering applications ranging from protein structure prediction to molecular simulations and material generation.
This paper provides a comprehensive and self-contained overview of the field of Geometric GNNs for 3D atomic systems.
arXiv Detail & Related papers (2023-12-12T18:44:19Z) - Machine learning detects terminal singularities [49.1574468325115]
Q-Fano varieties are positively curved shapes which have Q-factorial terminal singularities.
Despite their importance, the classification of Q-Fano varieties remains unknown.
In this paper we demonstrate that machine learning can be used to understand this classification.
arXiv Detail & Related papers (2023-10-31T13:51:24Z) - Toward random tensor networks and holographic codes in CFT [0.0]
In spherically symmetric states in any dimension and more general states in 2d CFT, this leads to a holographic error-correcting code.
The code is shown to be isometric for light operators outside the horizon, and non-isometric inside.
The transition at the horizon occurs due to a subtle breakdown of the Virasoro identity block approximation in states with a complex interior.
arXiv Detail & Related papers (2023-02-05T18:16:02Z) - Hyper-optimized approximate contraction of tensor networks with
arbitrary geometry [0.0]
We describe how to approximate tensor network contraction through bond compression on arbitrary graphs.
In particular, we introduce a hyper-optimization over the compression and contraction strategy itself to minimize error and cost.
arXiv Detail & Related papers (2022-06-14T17:59:16Z) - Boundary theories of critical matchgate tensor networks [59.433172590351234]
Key aspects of the AdS/CFT correspondence can be captured in terms of tensor network models on hyperbolic lattices.
For tensors fulfilling the matchgate constraint, these have previously been shown to produce disordered boundary states.
We show that these Hamiltonians exhibit multi-scale quasiperiodic symmetries captured by an analytical toy model.
arXiv Detail & Related papers (2021-10-06T18:00:03Z) - Quantum Annealing Algorithms for Boolean Tensor Networks [0.0]
We introduce and analyze three general algorithms for Boolean tensor networks.
We show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer.
We demonstrate that tensor with up to millions of elements can be decomposed efficiently using a DWave 2000Q quantum annealer.
arXiv Detail & Related papers (2021-07-28T22:38:18Z) - Emergent Einstein Equation in p-adic CFT Tensor Networks [6.127256542161883]
We show that a deformed Bruhat-Tits tree satisfies an emergent graph Einstein equation in a unique way.
This could provide new insights into the understanding of gravitational dynamics potentially encoded in more general tensor networks.
arXiv Detail & Related papers (2021-02-24T02:03:38Z) - Motif Learning in Knowledge Graphs Using Trajectories Of Differential
Equations [14.279419014064047]
Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks.
Many KGEs use the flat geometry which renders them incapable of preserving complex structures.
We propose a neuro differential KGE that embeds nodes of a KG on the trajectories of Ordinary Differential Equations (ODEs)
arXiv Detail & Related papers (2020-10-13T20:53:17Z) - Tensor network models of AdS/qCFT [69.6561021616688]
We introduce the notion of a quasiperiodic conformal field theory (qCFT)
We show that qCFT can be best understood as belonging to a paradigm of discrete holography.
arXiv Detail & Related papers (2020-04-08T18:00:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.