Entanglement distillation of boundary states of large N SU(N)1, Chern-Simons theory and Riemann surfaces
Howard J. Schnitzer

TL;DR
This paper introduces a tree tensor network method for entanglement distillation in large N SU(N)1 Chern-Simons theory and Riemann surfaces, providing insights into entanglement entropy in complex quantum systems.
Contribution
It proposes a novel tensor network approach for entanglement distillation in specific topological quantum field theories, extending previous proposals.
Findings
Successfully applied to bipartite systems with entanglement entropy log N
Demonstrates the effectiveness of the tensor network in complex topological settings
Provides a new computational tool for studying entanglement in large N theories
Abstract
A tree tensor network is proposed for the entanglement distillation of large N SU(N)1 Chern-Simons theory and Riemann surfaces, adopting a proposal of Bao, et al. This is illustrated for the entanglement entropy S(A) of a bipartite many-body system A, where here S(A) = log N.
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Taxonomy
TopicsBlack Holes and Theoretical Physics · Quantum many-body systems · Quantum Chromodynamics and Particle Interactions
Entanglement distillation of boundary states of large , Chern-Simons theory and Riemann surfaces
Howard J. [email protected]
Department of Physics
Brandeis University
Waltham
MA 02454
Abstract
A tree tensor network is proposed for the entanglement distillation of large Chern-Simons theory and Riemann surfaces, adapting a proposal of Bao, et al. This is illustrated for the entanglement entropy of a bipartite many-body system , where here .
I Introduction
There is a mapping , the functional integral, for Chern-Simons theory which relates the 3-dimensional manifold to a probability amplitude . If has a boundary, the path integral selects a state in a Hilbert space associated to the boundary field configurations. For Chern-Simons theory one can construct generators of the Clifford group and stabilizer states, and compute the entanglement entropy of a bipartite many-torus subsystem with only a single replica [1]. [2] show that
[TABLE]
for [1].
Entanglement distillation, as presented by \mtextciteBaoSet,*Bao:2018pvs,*Bao2019, describes a state in the boundary Hilbert space, which can be approximated by a state to arbitrary precision, by means of a tree tensor network relating the bulk and boundary theories. We explore this strategy for Chern-Simons theory in this paper.
II Entanglement distillation
Entanglement distillation, as discussed by \mtextciteBaoSet, presents a general procedure for constructing tensor networks for geometric states, which we adapt for a CFT/Chern-Simons theory correspondence. We review, and paraphrase, the essential features needed for our discussion.
Consider a state in large CFT, for a many-torus subsystem , with the entanglement entropy of between and its complement . For in the large limit, this is described by (I.1) and (I.2). In addition to the von Neumann entropy, consider the Rényi entropies,
[TABLE]
where is the density matrix of the subsystem . is a monotonically decreasing function of , where
[TABLE]
Smooth min. and max. entropies satisfy [1, 3]
[TABLE]
and
[TABLE]
which is flat to leading order in large . There exists a normalized state within trace distance of satisfying
[TABLE]
The constraints on smooth min. and max. entropies for the multitorus Hilbert space permit entanglement distillation, where a bipartite state is approximated by a nearby state in which the entanglement between two regions is made manifest. The properties of smooth min. and max. entropies provide an approximate state
[TABLE]
where is an approximate division of eigenvalues into blocks
[TABLE]
of width
[TABLE]
for . Then
[TABLE]
where approximates to the accuracy of (II.13).
Introduce auxiliary Hilbert spaces to distill EPR states and with dimensions
[TABLE]
which define maps and , where the bond dimensions are at large . These maps are given by
[TABLE]
and
[TABLE]
where and are isometries which embed Hilbert spaces of size
[TABLE]
and
[TABLE]
and their complex conjugates into the physical space .
For a bipartite state of
[TABLE]
with
[TABLE]
and
[TABLE]
Then is a tree tensor network of the form
[TABLE]
with
[TABLE]
and
[TABLE]
Identify
[TABLE]
where is the fusion matrix of satisfying , where and label the number of boxes of a single column Young tableau representation of , and where . For the distilled state, to leading order in large ,
[TABLE]
Therefore (II.20)–(II.28) represents a bipartite state on to accuracy.
As an application we can discuss this in the context of a genus two Hilbert space and a two-torus Hilbert space . There is an isometry [2]
[TABLE]
If is a unitary operator on , then
[TABLE]
is unitary on . A representation of the genus two Riemann surface without boundaries is given by an octagon with identified sides labeled by
[TABLE]
Consider a bipartite division of this surface, where the isometry induces a bipartite division of . [See Fig. 3 of ref. [2].] For the distilled state on , is approximated on to the same accuracy as on . There is an analogous discussion for , reflecting that (I.1) and (I.2) holds for a many-torus system.
III Concluding remark
We proposed a tree tensor network for the entanglement distillation for large Chern-Simons theory for a bipartite many-torus system. The next step in this program is the extension of the analysis to multipartite systems [4, 5, 6], in particular for stabilizer states. Note that equation (I.1) is reminiscent of the RT [7] and HRT [8] relations for holography.
Other related applications of entanglement in Chern-Simons theory are discussed in [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20].
IV Acknowledgements
We thank Isaac Cohen, Alastair Grant-Stuart, and Andrew Rolph for their assistance in the preparation of the paper, which provides a contribution to the author’s 85th birthday. {Bao:2018pvs,Bao2019}
\inset
BaoSet
\insetBaoSet
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1[1] Howard J. Schnitzer, 2019 ar Xiv: 1903.06789 v 1 [hep-th]
- 2[2] Grant Salton, Brian Swingle and Michael Walter In Phys. Rev. D 95.10 , 2017, pp. 105007 DOI: 10.1103/Phys Rev D.95.105007 · doi ↗
- 3[3] Ning Bao, Geoffrey Penington, Jonathan Sorce and Aron C. Wall, 2018 ar Xiv: 1812.01171 [hep-th]
- 4[4] Sepehr Nezami and Michael Walter, 2016 ar Xiv: 1608.02595 [quant-ph]
- 5[5] Ning Bao, Sepehr Nezami, Hirosi Ooguri, Bogdan Stoica, James Sully and Michael Walter In JHEP 09 , 2015, pp. 130 DOI: 10.1007/JHEP 09(2015)130 · doi ↗
- 6[6] Temple He, Matthew Headrick and Veronika E. Hubeny, 2019 ar Xiv: 1905.06985 [hep-th]
- 7[7] Shinsei Ryu and Tadashi Takayanagi In Phys. Rev. Lett. 96 , 2006, pp. 181602 DOI: 10.1103/Phys Rev Lett.96.181602 · doi ↗
- 8[8] Veronika E. Hubeny, Mukund Rangamani and Tadashi Takayanagi In JHEP 07 , 2007, pp. 062 DOI: 10.1088/1126-6708/2007/07/062 · doi ↗
