aainstitutetext: Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA 91125, USAbbinstitutetext: Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA 91125, USA
Entanglement Entropy and its Quench Dynamics for Pure States of the Sachdev-Ye-Kitaev model
Sachdev-Ye-Kitaev (SYK) is a concrete solvable model with non-Fermi liquid behavior and maximal chaos. In this work, we study the entanglement Rényi entropy for the subsystems of the SYK model in the Kourkoulou-Maldacena states. We use the path-integral approach and take the saddle point approximation in the large- limit. We find a first-order transition exist when tuning the subsystem size for the case, while it is absent for the case. We further study the entanglement dynamics for such states under the real-time evolution for noninteracting, weakly interacting and strongly interacting SYK(-like) models.
1 Introduction
In recent years, the entanglement entropy and its dynamics in many-body systems have drawn a lot of attention. As an example,
the entanglement entropy has been studied both theoretically Deutsch (1991); Srednicki (1994); Calabrese and Cardy (2004); Garrison and Grover (2018) and experimentally Islam et al. (2015); Li et al. (2017) for interacting quantum systems that satisfy the eigenstate thermalization hypothesis (ETH). For a general energy eigenstate, it shows volume law scaling, in contrast to the area law scaling in the many-body localization (MBL) phase Bauer and Nayak (2013); Lukin et al. (2019). The entanglement dynamics can also be related to the out-of-time-order correlators Hosur et al. (2016); Fan et al. (2017), which characterize the scrambling of quantum information Lashkari et al. (2013); Kitaev (2014); Ho and Abanin (2017); Mezei and Stanford (2017). Moreover, the recent resolution of the information paradox Penington (2019); Almheiri et al. (2019a, b, c, d); Penington et al. (2019) is directly from the refined understanding of the Ryu-Takayanagi formula Ryu and Takayanagi (2006a, b); Lewkowycz and Maldacena (2013) for computing the entanglement entropy in holographic systems.
Unfortunately, the calculation of the entanglement entropy for many-body systems is usually hard. Toy models where entanglement entropy can be computed efficiently are of especial interest. One strategy is to construct random unitary dynamics Hayden et al. (2016); Nahum et al. (2017); Von Keyserlingk et al. (2018).
Here, we consider an alternative route by studying a specific solvable model named the Sachdev-Ye-Kitaev model Sachdev and Ye (1993); Kitaev (2014); Maldacena and Stanford (2016); Kitaev and Suh (2018), which describes Majorana modes with infinite range body interaction. For , it is known as a non-Fermi liquid without quasiparticles which shows maximal chaotic behavior. In the low-energy limit, the system is described in terms of reparametrization modes, with an effective Schwarzian action Kitaev and Suh (2018); Maldacena and Stanford (2016); Kitaev (2014). The same action also shows up for the Jackiw-Teitelboim gravity in 2D Kitaev and Suh (2018); Maldacena et al. (2016).
Previously, there are studies of the SYK model from the entropy perspective. Assuming the system satisfies ETH Deutsch (1991); Srednicki (1994); Hunter-Jones et al. (2018); Haque and McClarty (2019); Sonner and Vielma (2017), the entanglement entropy can be related to the thermal entropy with an effective temperature depending on the system size Huang et al. (2019). An analytical approximation has also been derived based on the many-body spectrum García-García and Verbaarschot (2017). Numerically, the subsystem entropy has been studied in Liu et al. (2018) by exact diagonalization for the ground state, and in Zhang et al. (2020); Haldar et al. (2020) by path-integral approach for thermal ensembles. There are also studies for two copies of (coupled) SYK models prepared in the thermofield double state, which purifies the thermal density matrix Gu et al. (2017); Penington et al. (2019); Chen et al. (2020). There are also studies for random hopping model, which is directly related to the SYK2 case Magán (2016a, b). However, the large- microscopic entanglement entropy and its dynamics for pure states of a single SYK model are still unknown 111There are related dicussions of entanglement entropy in Magán (2017)..
In this work, we establish the formulation for computing Rényi entanglement entropy for the Kourkoulou-Maldacena pure states in SYK-like models Kourkoulou and Maldacena (2017). The paper is organized as follows: In section 2, we give a brief review of the SYK model and the Kourkoulou-Maldacena states. We then derive the path-integral representation of Rényi entanglement entropy for such pure states in section 3. The numerical results are presented in section 4. By varying the subsystem size, we find a first-order transition of the entanglement entropy for the SYK4 model, which leads to the singularity of the Page curve at half system size Page (1993). This is qualitatively different from the case, where the entanglement entropy changes analytically when varying the subsystem size. We further study the exact dynamics of the entanglement entropy for the SYK4 non-Fermi liquid, with a comparison to noninteracting or weakly interacting SYK2 Fermi liquids in section 5. Finally, we summarize our results in 6.
2 SYK model and Kourkoulou-Maldacena states
The Hamiltonian of SYKq model Kitaev (2014); Maldacena and Stanford (2016) reads:
(1)
Here is an even integer and labels different Majorana modes. We take the convention that . are independent Gaussian variables with:
(2)
Here is taken to be a constant in the large- limit.
For a thermal ensemble, to the leading order of , the two-point correlator satisfies the self-consistent equation:
(3)
where the self-energy is given by melon diagrams. By solving the Schwinger-Dyson equation, the model is found to be a Fermi liquid with finite spectral function near for . On contrary, for , the model has divergent spectral function , which is known as a non-Fermi liquid. Further study shows it has maximal chaos Kitaev (2014); Maldacena and Stanford (2016) and satisfies the ETH Hunter-Jones et al. (2018); Haque and McClarty (2019); Sonner and Vielma (2017).
Considering the SYK system in some eigenstate with energy , the entropy of the a subsystem containing () Majorana fermions is argued to be Huang et al. (2019):
(4)
for . Here is the thermal entropy density in the micro-canonical ensemble with energy density . A similar statement when the total system is prepared in a thermal ensemble has been tested in Zhang et al. (2020). Approximately, we have with being the energy density of the ground state García-García and Verbaarschot (2017). On the other hand, for , the ground state entanglement entropy can be calculated analytically Liu et al. (2018); Zhang et al. (2020).
In this work, we focus on a specific class of pure states of the SYK model Kourkoulou and Maldacena (2017). These states are now known as the Kourkoulou-Maldacena (KM) states. To construct them, we first pair Majorana fermions as with 222Since there is a permutation symmetry for different modes , this choice is general.. Then (unnormalized) KM states are given by:
(5)
Here and . We could always redefine to set for all . As result, all single states are equivalent after averaging over the ensemble of random interaction. We will focus on for most parts of the manuscript. Moreover, for simplicity, from now on we keep the dependence of implicit.
The hallmark of these states is that to the leading order of , the correlation functions of can be related to thermal correlators Kourkoulou and Maldacena (2017), under the assumption of the disorder replica diagonal Fu and Sachdev (2016); Gur-Ari et al. (2018); Kitaev and Suh (2018); Gu et al. (2019). As an example, two-point functions
with , can be expressed in terms of the thermal Green’s function . Explicitly, all non-zero components are
(6)
Here we have . This shows that the diagonal component take the same form as a thermal Green’s function, while the off-diagonal part characterize the deviation from a thermalized state (at the two-point function level). For , selects one state from the ground state sector of the SYK model, and .
We are mainly interested in the Rényi entanglement entropy of such pure states. For such states, we define subsystem A consisting of complex fermions 333Note that although the KM pure states are expected to be dual to an AdS2 geometry with a brane, there is no index degree of freedom, and consequently no direct analogy of this entanglement entropy in the gravity picture.. The reduced density matrix is given by tracing out its complimentary . The th Rényi entropy is then given by
(7)
with being the Von Neumann entropy. Since the full system is in a pure state, we expect being symmetric under a reflection along . For , is a product state, and we have . On the contrary, if we take , and we expect follow a Page curve Page (1993) with energy density depending on subsystem size (4).
3 Path-integral for pure-state entanglement entropy
In this section, we derive the path-integral representation of for KM pure states. We begin with a warm up by computing the normalization factor in section 3.1, which is also needed then computing the entanglement entropy. The path integral formula for computing is then derived in 3.2.
3.1 A warm up:
Let us first consider the path-integral representation of . Note that this is in fact not essential, since can be directly related to the the thermal partition function Zhang et al. (2020). However, the trick developed in this subsection is useful when computing the entanglement entropy.
The state is given by an imaginary-time evolution of a initial state . The graphic representation is:
(8)
Here we have explicitly separated out fermions with odd/even indices: represents Majorana fermions with odd/even indices. The solid lines denote the imaginary-time evolution and the dotted lines represent interactions between fermions. Two points connected by the dotted line are at the same imaginary time. The black dots represent the boundary condition , or in terms of Majorana fermions . Similarly, the normalization is given by
(9)
Here we have another boundary condition at imaginary time . The path-integral representation of is then
(10)
Here b.c. indicates the the boundary condition at and :
(11)
We further take the disorder average of random interaction . As for the thermal ensemble, we expect the replica diagonal assumption works well in the large- limit and we could neglect the difference between and . Consequently, we keep the disorder average implicitly from now on.
To proceed, we use the standard trick by introducing bilocal fields and Maldacena and Stanford (2016). Since fields with even or odd indices are in-equivalent, we should define two sets of fields and . The definition of and is
(12)
and are introduced in order to impose the relation between and :
(13)
Then by integrating out the Majorana fields, we find
(14)
Here the effective - action is given by 444There could be additional boundary terms. Nevertheless, they cancel out when computing the entanglement entropy.
(15)
Here b.c. denotes the boundary condition 11. Note that although the self-energy is blocked diagonal, the boundary condition would mix modes with even/odd indices. In the large- limit, we take the saddle point of (22). The saddle point equation reads
(16)
Here we have also introduced
for completeness. In terms of bilocal fields with , the boundary condition (11) becomes
(17)
Solving the equation (16) with boundary condition (17), and substituting the solution into (22) already gives the on-shell action and thus . However, it is more convenient to introduce a different parametrization of the contour. The key observation is that if we define a Majorana field with parameter :
(18)
here , the boundary condition (11) becomes the traditional continuous and anti-periodic boundary condition , as for a thermal ensemble. The Green’s function is then given by
(19)
The self-consistent equation for is then
(20)
Moreover, both the action (22) and boundary condition (11) are invariant under and . As a result, we have . Instead of (16), we can then use
(21)
Here is a projector. We have if both and represents the same field ( or ) and otherwise zero. This definition for is more general if we choose a different point on the contour (18). Note that comparing to a thermofield double state with inverse temperature and fermions, the main difference is the presence of , which breaks the time translational invariance.
We can then choose to solve the equation (21) and self-consistently. Moreover, we could also express the on-shell action in terms of and :
(22)
This gives an alternative route to compute .
3.2 Computing
Having illustrated the trick of parameterizing the contour by , we consider the path-integral representation of in this subsection.
To compute , we first consider the path-integral representation of . Separating out the modes in system and , a graphic representation is
(23)
here the red/blue solid line represents the contour for subsystem . represents Majorana fermions in subsystem with odd/even indices, with .
The unnormalized density matrix is then given by tracing out the subsystem or, graphically, by connecting the (blue) contours of . can then be computed by sewing copies of . To be concrete, in this work we focus on the case. The corresponding contour is then given by
(24)
where the symmetry of interchanging and becomes obvious. Here we have parametrized the contour by anticlockwise. We define for and otherwise. Similar to the previous section, the boundary condition again becomes the traditional continuous and anti-periodic boundary condition for :
(25)
Similar to the previous subsection, we consider the Green’s function for :
The Schwinger-Dyson equation then reads
(26)
Here the labels different boundary conditions for subsystem. This self-energy can be directly understood by the melon diagram (Here for example):
(27)
with or being the probability of having a mode in subsystem or . We have if both and represents the same field ( or ) and otherwise zero. Note that the main difference between this expression of self-energy and that for computing the subsystem Rényi entropy of thermal ensembles Zhang et al. (2020) is the presence of .
These set of equations for and can also be directly derived by writing out the action and taking the saddle point approximation. Consequently, after solving (26), we have with:
(28)
We have . The second Rényi entanglement entropy is then given by
(29)
The generalization of above discussions to -th Rényi entropy is straightforward.
4 Numerical results
Because of the lack of translational invariance, the analytical study of (26) is difficult. In this section, we present numerical results for the entanglement entropy with different and .
We perform the numerical iteration of (26) similar to that in Chen et al. (2020); Zhang et al. (2020). We discretize the time into points, with . For , we typically take . The equation (26) then becomes a matrix equation:
(30)
Here and are the Green’s functions without interaction on the contour (24). Their elements are either or depending on the time ordering and the connectivity of contours. Explicitly:
(31)
The on-shell action is then
(32)
Here we have used to enable the convergence. The extrapolation towards is performed finally.
Figure 1: (a). The entanglement entropy of KM states at different temperature with . The black dashed line is the analytical approximation for (4): with . The red dashed line is the subsystem entropy for a thermal ensemble with Zhang et al. (2020). (b). The entanglement entropy of KM states at different temperature with . The black dashed line is the analytical formula for the SYK2 ground state Zhang et al. (2020).
Now we present numerical results for the entanglement entropy of KM pure states. We first focus on the SYKq model with or as an example of strongly interacting systems or non-interacting systems.
The result of for different with is shown in Figure 1 (a). We have also plotted the analytical approximation Huang et al. (2019) as the black dashed line and the subsystem entropy for a thermal ensemble with Zhang et al. (2020) as the red dashed line. For small , the entanglement builds up quickly as increases, and is an analytical function of . On the other side, for large near , there exist two different saddle point solutions, and the coexistence region becomes larger as increases. The true curve for is determined by the comparing actions of different saddles, leading a first-order transition. As we will see in the next section, these two saddle points smoothly connected to the subsystem entropy or of a thermal ensemble at corresponding temperature. For , the numerical result of the KM state gives , which is larger than the analytical approximation , but also a little smaller than the thermal ensemble result . We attribute this to the fact that KM states are non-thermal. This is to be compared with the quadratic Hamiltonian case with shown in Figure 1 (b), where is analytical even for very low temperature.
The existence of the transition gives rise to the singularity of the Page curve at Page (1993), as expected for general chaotic systems. Consequently, the existence of transition in highly entangled pure states to be a general feature for interacting systems with saddle-point description, including different generalizations of the SYK model.
We then consider dependence for . Here we fix . The numerical results are shown in Figure 2 (a). The and case has been discussed above. If we further increase , we find the entanglement entropy decreases rapidly as increases. This can be understood that in the large limit, the system is weakly interacting. As a result, for fixed the system becomes less entangled as increases. It is also interesting to notice that for larger , becomes much more flat near , which can also be seen from Figure 2 (b). There is an analogy phenomenon for the entanglement entropy under the random Hamiltonian evolution You and Gu (2018). It is also reasonable that for larger , the Hamiltonian becomes denser and resembles a random Hamiltonian.
Figure 2: (a). The entanglement entropy of KM states at different with . The black dashed line is the maximal entropy: with . (b). The derivative of the entanglement entropy of KM states at different with . The black dashed line represent the maximal entropy .
5 Quench dynamics for pure-state entanglement entropy
We now turn to the study of real-time entanglement dynamics by considering the evolution of . The normalized state after evolving time is
(33)
We first consider the two-point function of on real-time. There are studies on quench dynamics for two-point functions for different SYK-like models in the large- limit Eberlein et al. (2017); Haldar et al. (2019); Kuhlenkamp and Knap (2019); Zhang (2019); Almheiri et al. (2019e) by solving the Kadanoff-Baym equation Stefanucci and Van Leeuwen (2013) 555In these works, the initial state is a thermal ensemble. Here we instead of focus on pure states.. However, in our case the two point function can be directly obtained by an analytical continuation of (6). Consequently, the diagonal components are again thermal at any time while the off-diagonal components in the long-time limit. Here is the decay rate of the real-time two-point function on thermal ensemble. In the low-temperature limit, we have and . In the high-temperature limit, we instead have , which leads to a thermalization time .
We would like to study this quenching process from the entanglement perspective. To compute the evolution of the entanglement entropy, we again apply the path integral representation using the contour in (24). The main difference is that now the solid lines can be either (forward/backward) real or imaginary-time evolution:
(34)
Here we draw an arrow for the direction of real-time evolutions. When the parametrization is along the same direction as the arrow, the real-time evolution is effectively forward. Otherwise, the evolution is backward. Since the evolution is governed by for imaginary-time evolution and for forward/backward real-time evolution, we need to add additional factor of . This leads to the modification of (26):
(35)
where now we have , including both the real-time and the imaginary-time evolution. Here with or for being a parameter of an imaginary-/forward real- or backward real-time evolution. After solving (35), the on-shell action can still be computed as (28).
We first consider the case with . Since the system satisfy the ETH, we expect when the entanglement entropy approaches the subsystem entropy of a thermal ensemble in the long time limit. This can be understood by expanding
Here are eigenstates of the Hamiltonian with eigenenergy . The entanglement entropy for a subsystem can be written as the expectation of the swap operator on two replicas of the original system:
(36)
Here is the swap operator of subsystem on doubled Hilbert space. Consequently, in the long time limit, we have
(37)
We have used the fact that the diagonal ensemble is approximately a thermal ensemble (of the full system) with inverse temperature for systems satisfying the ETH. Previous study shows for while for Zhang et al. (2020). Note that this is a non-trival statement since the effective temperature depends on for non-local Hamiltonians (4). Consequently, in the large- limit, we have for and for .
Figure 3: (a). The dynamics of the Rényi entanglement entropy as a function of subsystem size with and . The black dashed reference line is and . (b). The dynamics of the Rényi entanglement entropy as a function of subsystem size with and . The black dashed line is the analytical formula for the SYK2 ground state Zhang et al. (2020).
The numerical result for with is shown in Figure 3 (a). Here we have set . Similar to tunning the imaginary-time , we find a first-order transition in the long real-time limit. By comparing with and , two different saddles in the long-time limit can be identified with the contribution from or in (37). We have also checked the match for finite .
We could also understand the two saddle points directly from the path-integral representation (34). For convenience, here we put back the possible dependence of KM states. we write . In the long-time limit, the saddle point solutions can be understood as follows: We first consider turn off the interaction between subsystem and . Then the Green’s functions and become block diagonal according to . When we turn on the interaction between subsystem and , one saddle is given by: We keep almost replica diagonal. Two half contours of that interacts with the same subsystem become effectively connected Saad et al. (2018); Penington et al. (2019); Chen et al. (2020), while two interact with different subsystems becomes less correlated far away from the boundary of the contour. Graphically, this means
(38)
Here the contours within each box are effectively connected. The red dots represent the insertion of field . An important observation is that since the correlation is small between two boxes near the black dots where the boundary condition is imposed, we could make the approximation:
(39)
Here we drop the arrow for simplicity. Physically, this may be understood as follows: if we evolve for a long time, the reduced density matrix of subsystem always becomes thermal, and we can not distinguish different initial states or . If we sum over all possible boundary states, the contour becomes:
(40)
Here we have used the relation
(41)
and we have merged the contour for and for both subsystem . This is exactly the contour for computing the subsystem Rényi entropy of a thermal ensemble Zhang et al. (2020). Similarly, if we exchange the role of and subsystem, we get another saddle point corresponds to .
Note that this existence of the first-order transition can be viewed as an analogy of the information paradox in more complicated set-ups Penington (2019); Almheiri et al. (2019a, b, c, d); Penington et al. (2019); Penington (2019); Chen et al. (2020): if we consider increasing and time from and , we could follow the saddle of without facing any singularity till , which leads to an information paradox. The solution to the information paradox is also similar: at some time , a new saddle-point appears which preserves the unitary. For setups with clear bulk description, this new saddle corresponds to a solution with islands Penington (2019); Almheiri et al. (2019a, b, c, d); Penington et al. (2019); Penington (2019).
For , the system shows qualitatively different behaviors. Since the state is annihilated by , is then annihilated by . For long time , we expect this to be a random superposition with some parameter . An remarkable observation is that this is just the ground state of SYK2 model, and the entanglement entropy is again given in Zhang et al. (2020). As shown in Figure 3 (b), the arguments works even for , which means the system would not thermalize to an infinite temperature ensemble , as expected for non-interacting systems.
Figure 4: (a). The dynamics of the Rényi entanglement entropy as a function of subsystem size with and . The black dashed line is the maximal entropy: with . (b). The dynamics of the Rényi entanglement entropy as a function of subsystem size with and . The black dashed line is the maximal entropy with . The black dashed line is the analytical formula for the SYK2 ground state Zhang et al. (2020).
We further consider adding SYK4 random interaction term to the SYK2 random hopping model Banerjee and Altman (2017); Chen et al. (2017); Song et al. (2017). The Hamiltonian reads
(42)
Here to avoid possible confusion, we have changed the notion of the random hopping parameters to be with variance . Near the SYK4 fixed point, the random hopping term is relevant. As a result, the system is always a non-Fermi liquid for at low temperature . At finite temperature, there is a crossover between the SYK2 and the SYK4 fixed points Chen et al. (2017).
We could similarly define the KM pure states for this model and study the entanglement dynamics with minor modification of (35):
(43)
The evolution of for different is shown in Figure 4. We find that the entanglement show different behaviors for and . For the behavior is basically the same as the SYK4 case in Figure 3 (a), as shown in Figure 4 (a). The reason is that the random hopping term plays an role only in the long time limit , when the entanglement has already been built up.
This is to be compared with the case shown in Figure 4 (b). In this case, the system builds up the entanglement in two steps. The short time behavior is governed by the random hopping term, leading to a close to the SYK2 ground state. Then the entanglement continues to increase linearly but with much slower speed until the system thermalizes. This can also been seen from a plot for with different time . As shown in Figure 5 (a), the entanglement entropy with and increases rapidly for , while it increases linearly with a smaller slope for until its saturation. The slope of the linear growth is proportional to , which can be seen from Figure 5 (b). This can be understood as a perturbative calculation near the solution, similar to the short-time behavior in Chen et al. (2020); Gu et al. (2017).
Figure 5: (a). The Rényi entanglement entropy as a function of time for different subsystem size . Here we take and . (a). The Rényi entanglement entropy as a function of time for different subsystem size . Here we fix and . The black dashed lines are linear fits for the linear growth region.
6 Conclusion
In this work, we study the entanglement Rényi entropy of Kourkoulou-Maldacena pure states of the SYK model, including its generalizations. We use the path-integral approach which gives the exact entanglement entropy in the large- limit.
At low energy density, we find a first-order transition for the entanglement entropy for the SYK4 model when tuning the subsystem size . This is different compared to the case where the entropy is a smooth function. Similar behaviors exist if we consider long real-time evolution. The first-order transition is from the existence of two different saddle points, which corresponds to the thermal Rényi entropy of reduced density matrices for different subsystems. We further consider adding small SYK4 random interaction to the SYK2 case, leading to the slow linear growth of entanglement entropy in the intermediate time regime.
Acknowledgement. We thank Xiao Chen, Yingfei Gu, Chunxiao Liu for discussion. PZ acknowledges support from the Walter Burke Institute for Theoretical Physics at Caltech.
Hosur et al. (2016)P. Hosur, X.-L. Qi,
D. A. Roberts, and B. Yoshida, Journal of High Energy
Physics 2016, 4
(2016).
Fan et al. (2017)R. Fan, P. Zhang, H. Shen, and H. Zhai, Science bulletin 62, 707 (2017).
Lashkari et al. (2013)N. Lashkari, D. Stanford,
M. Hastings, T. Osborne, and P. Hayden, Journal of High Energy Physics 2013, 22 (2013).
Kitaev (2014)A. Kitaev, “Hidden correlations
in the hawking radiation and thermal noise, talk given at the 2015
breakthrough prize fundamental physics symposium,” (2014).
Von Keyserlingk et al. (2018)C. Von Keyserlingk, T. Rakovszky, F. Pollmann,
and S. L. Sondhi, Physical Review
X 8, 021013 (2018).
Sachdev and Ye (1993)S. Sachdev and J. Ye, Physical review
letters 70, 3339
(1993).
Maldacena and Stanford (2016)J. Maldacena and D. Stanford, Physical Review D 94, 106002 (2016).
Kitaev and Suh (2018)A. Kitaev and S. J. Suh, Journal
of High Energy Physics 2018, 183 (2018).
Maldacena et al. (2016)J. Maldacena, D. Stanford,
and Z. Yang, Progress of
Theoretical and Experimental Physics 2016
(2016).
Hunter-Jones et al. (2018)N. Hunter-Jones, J. Liu, and Y. Zhou, Journal of High Energy
Physics 2018, 142
(2018).
Haque and McClarty (2019)M. Haque and P. A. McClarty, Physical Review B 100, 115122 (2019).
Sonner and Vielma (2017)J. Sonner and M. Vielma, Journal of High Energy Physics 2017, 149 (2017).
Huang et al. (2019)Y. Huang, Y. Gu, et al., Physical Review D 100, 041901 (2019).
García-García and Verbaarschot (2017)A. M. García-García and J. J. Verbaarschot, Physical Review D 96, 066012 (2017).
Liu et al. (2018)C. Liu, X. Chen, and L. Balents, Physical Review B 97, 245126 (2018).
Zhang et al. (2020)P. Zhang, C. Liu, and X. Chen, arXiv preprint arXiv:2003.09766 (2020).
Haldar et al. (2020)A. Haldar, S. Bera, and S. Banerjee, arXiv preprint
arXiv:2004.04751 (2020).
Gu et al. (2017)Y. Gu, A. Lucas, and X.-L. Qi, Journal of High Energy Physics 2017, 120 (2017).
Chen et al. (2020)Y. Chen, X.-L. Qi, and P. Zhang, arXiv preprint arXiv:2003.13147 (2020).
Magán (2016a)J. M. Magán, Journal of High Energy Physics 2016, 81 (2016a).
Magán (2016b)J. M. Magán, Physical review letters 116, 030401 (2016b).
Magán (2017)J. M. Magán, Physical Review D 96, 086002 (2017).
Kourkoulou and Maldacena (2017)I. Kourkoulou and J. Maldacena, arXiv preprint arXiv:1707.02325 (2017).
Page (1993)D. N. Page, Physical
review letters 71, 1291
(1993).
Fu and Sachdev (2016)W. Fu and S. Sachdev, Physical Review
B 94, 035135 (2016).
Gur-Ari et al. (2018)G. Gur-Ari, R. Mahajan, and A. Vaezi, Journal of High
Energy Physics 2018, 70
(2018).
Gu et al. (2019)Y. Gu, A. Kitaev, S. Sachdev, and G. Tarnopolsky, arXiv preprint arXiv:1910.14099 (2019).
You and Gu (2018)Y.-Z. You and Y. Gu, Physical Review
B 98, 014309 (2018).
Eberlein et al. (2017)A. Eberlein, V. Kasper,
S. Sachdev, and J. Steinberg, Physical Review B 96, 205123 (2017).
Haldar et al. (2019)A. Haldar, P. Haldar,
I. Mandal, and S. Banerjee, arXiv preprint arXiv:1903.09652 (2019).
Kuhlenkamp and Knap (2019)C. Kuhlenkamp and M. Knap, arXiv
preprint arXiv:1906.06341 (2019).
Zhang (2019)P. Zhang, Physical Review B 100, 245104 (2019).
Almheiri et al. (2019e)A. Almheiri, A. Milekhin,
and B. Swingle, arXiv preprint
arXiv:1912.04912 (2019e).
Stefanucci and Van Leeuwen (2013)G. Stefanucci and R. Van Leeuwen, Nonequilibrium
many-body theory of quantum systems: a modern introduction (Cambridge University Press, 2013).
Saad et al. (2018)P. Saad, S. H. Shenker, and D. Stanford, arXiv preprint
arXiv:1806.06840 (2018).
Banerjee and Altman (2017)S. Banerjee and E. Altman, Physical Review B 95, 134302 (2017).
Chen et al. (2017)X. Chen, R. Fan, Y. Chen, H. Zhai, and P. Zhang, Physical review letters 119, 207603 (2017).
Song et al. (2017)X.-Y. Song, C.-M. Jian, and L. Balents, Physical review
letters 119, 216601
(2017).