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The time-dependent variational principle (TDVP)

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The time-dependent variational principle[1][2] is an alternative to the Lie-Trotter decomposition employed in the local Krylov method. The motivation of this approach is quite different: its primary aim is to constrain the time evolution to a specific manifold of matrix-product states of a given initial bond dimension. To do so, it projects the action of the Hamiltonian into the tangent space to this manifold and then solves the TDSE solely within the manifold. While ideally used in its single-site variant, the two-site variant allows for flexibility in the bond dimension.


The main difference between the TDVP and the local Krylov method is in the derivation of the series of these local time-dependent Schrödinger equations and the recovery of the original-time step after each local forward update: Instead of simply projecting the original site tensor onto the new basis as done in the local Krylov approach, the TDVP explicitly solves a backwards-evolution equation. To embark on the derivation for the TDVP, we need to introduce a few additional ingredients: First, we define the single-site tangent space $ T_{|\psi\rangle}$ of a given MPS $|\psi\rangle$ as the space spanned by variations of single MPS tensors. One may e.g. change the first site tensor of the MPS keeping all others fixed to obtain a new state and combine the result with another MPS where only the second site tensor was changed, but one may not change two (or more) site tensors in the same basis state. The projector $\hat P_{T_{\ket{\psi}}}$ which projects onto this tangent space is given by[1][2] \begin{equation} \hat P_{T_{|\psi\rangle}} = \sum_{j=1}^L \hat P_{j-1}^{L, |\psi\rangle} \otimes \mathbf{\hat 1}^{\vphantom{L\ket{\psi}}}_j \otimes \hat P_{j+1}^{R,|\psi\rangle} - \sum_{j=1}^{L-1} \hat P_{j}^{L,|\psi\rangle} \otimes \hat P_{j+1}^{R,|\psi\rangle} \quad, \label{eq:tdvp:projector} \end{equation} where $\hat{P}^{L,\ket{\psi}}_j$ ($\hat{P}^{R,\ket{\psi}}_j$) projects on the sites left (right) of and including site $n$ and is exactly the same as the projectors used in the local Krylov method:


These projectors use the gauge-fixed left- and right-normalised MPS tensors, i.e. they depend on the MPS $|\psi\rangle$ and can be written as \begin{align} \hat P^{L, \ket{\psi}}_{j; \bar{\sigma}_1, \ldots, \bar{\sigma}_j, \sigma_1, \ldots, \sigma_j} & = \sum_{m_j} \bar{\psi}^{L; \bar{\sigma}_1, \ldots, \bar{\sigma}_j}_{j; m_j} \otimes \psi^{L; \sigma_1,\ldots, \sigma_j}_{j; m_j} \\ \hat P^{R, \ket{\psi}}_{j; \bar{\sigma}_j, \ldots, \bar{\sigma}_L, \sigma_j, \ldots, \sigma_L} & = \sum_{m_{j-1}} \bar{\psi}^{R; \bar{\sigma}_j, \ldots, \bar{\sigma}_L}_{j; m_{j-1}} \otimes \psi^{R; \sigma_j,\ldots, \sigma_L}_{j; m_{j-1}} \end{align} where $\psi^{L(R)}_{j}$ is the collection of left- (right-) normalised MPS tensors on site $j$ and to its left (right) as in the local Krylov approach. The first contributing sum filters for all MPS which differ at most on one site from $|\psi\rangle$, whereas the second contributing sum removes all those states which coincide with $|\psi\rangle$. Put differently, individual tangent vectors are constructed by replacing any orthogonality center tensor $M_j$ of the MPS by another tensor $N_j$ which is orthogonal to $M_j$, i.e., $M_j \cdot \bar{N}_{j} = 0$. In contrast to the projectors $\hat \Pi_j^{\ket{\psi}}$ of the local Krylov method, the total projector $\hat P_{T_{\ket{\psi}}}$ projects onto some subspace of the Hilbert space and is only coincidentally written as a sum of local terms.

Second, when inserting the projector $\hat P_{T_{|\psi\rangle}}$ into the TDSE, we obtain \begin{align} \frac{\partial}{\partial t}|\psi\rangle = & - \mathrm{i} \hat P_{T_{|\psi\rangle}} \hat H |\psi\rangle \\ = & - \mathrm{i} \sum_{j=1}^L \hat P_{j-1}^{L,|\psi\rangle} \otimes \mathbf{\hat 1}^{\vphantom{L\ket{\psi}}}_j \otimes \hat P_{j+1}^{R,|\psi\rangle} \hat H |\psi\rangle + \mathrm{i} \sum_{j=1}^{L-1} \hat P_{j}^{L,|\psi\rangle} \otimes \hat P_{j+1}^{R,|\psi\rangle} \hat H |\psi\rangle \label{eq:tdvp:tdse-projected}\;. \end{align} While an exact solution is still not possible, we can approximate it by solving each term individually and sequentially, i.e., solve $L$ forward-evolving equations of the form

\begin{align} \frac{\partial}{\partial t}|\psi\rangle &= - \mathrm{i} \hat P_{j-1}^{L,\ket{\psi}} \otimes \mathbf{\hat 1}^{\vphantom{L\ket{\psi}}}_j \otimes \hat P_{j+1}^{R,\ket{\psi}} \hat H |\psi\rangle \label{eq:tdvp:fw} \end{align} and $L-1$ backward-evolving equations of the form \begin{align} \frac{\partial}{\partial t}|\psi\rangle &= + \mathrm{i} \hat P_{j}^{L,\ket{\psi}} \otimes \hat P_{j+1}^{R,\ket{\psi}} \hat H |\psi\rangle \;.\label{eq:tdvp:bw} \end{align}

We then multiply each individual equation above by the single-site map $\bar{\psi}^L_{j-1} \otimes \bar{\psi}^R_{j+1}$ or the center-bond map $\bar{\psi}^L_{j} \otimes \bar{\psi}^R_{j+1}$, respectively. As a result, instead of having to work with the full MPS $|\psi\rangle$, we can work with the effective single-site and effective center matrix tensors and associated local Schr"odinger equations directly: \begin{align} \frac{\partial}{\partial t}M^{\vphantom{\mathrm{eff}}}_j &= - \mathrm{i} \hat H^{\mathrm{eff}}_{j} M^{\vphantom{\mathrm{eff}}}_j\label{eq:tdvp:fw:projected} \\ \frac{\partial}{\partial t}C^{\vphantom{\mathrm{eff}}}_{\underline{j}} &= + \mathrm{i} \hat H^\mathrm{eff}_{\underline{j}} C^{\vphantom{\mathrm{eff}}}_{\underline{j}} \quad.\label{eq:tdvp:bw:projected} \end{align} The tensor contraction in the RHS of the forward evolution is medium while the RHS of the backwards evolution is medium Each of these equations can be solved with a local application of the Krylov method much as in DMRG or the local Krylov method of the previous section.

Sweeping right-to-left (rather than left-to-right) through the system results in solving the equations in reverse order. This turns the initial first-order integrator into a second-order integrator, reducing the time step error (as described in below) from $O(\delta)$ to $O(\delta^2)$ if both sweeps are done with halved time steps $\delta/2$.

An interesting property of the single-site TDVP variant (1TDVP) is that the projection of the Hamiltonian onto the MPS manifold occurs before the time evolution, the projection is the only step necessary to obtain the Lie-Trotter decomposition of the Hamiltonian, and no truncation has to happen after the evolution. As such, both the norm and energy of the state are conserved under real-time evolution. This is in contrast to the local Krylov method, where the basis transformation generated by the $Q$ tensors is not part of the projector and hence introduces an additional error. Alternatively, it is straightforward to extend the mechanism to a two-site variant. This 2TDVP forward-evolves a local tensor $M_{(j,j+1)}$ which needs to be split into two separate site tensors again following the evolution. The advantage is that the bond dimension of the state can be adapted on the fly. However, norm and energy are now no longer conserved exactly if a truncation of the evolved bond is necessary.


The TDVP has four sources of errors: firstly, there is a projection error due to the projection of the full time-dependent Schrödinger equation (TDSE) onto the MPS manifold of limited bond dimension. This error is particularly severe if the MPS in question has a small bond dimension, but it is exactly zero if the MPS has maximal (exponentially growing) bond dimension. However, the projection error occurs during the projection of the TDSE onto the relevant subspace, i.e., before the time evolution. As such, it cannot lead to a violation of energy conservation or change the norm of the time-evolved state (during real-time evolution). Using a two- or multi-site variance[3] it is possible to estimate this projection error. If the $n$-site variance of the state is large, the $(n-1)$TDVP will provide inadequate results. Vice versa, if the up-to-$n$-site variance of a state is small, the $n$TDVP will consider this state an eigenstate of the Hamiltonian and the time evolution will only add a global phase to the state. As a corollary, the 2TDVP can evolve Hamiltonians with only nearest-neighbor interactions without incurring a projection error.

Second, the chain of forwards and backwards evolutions can be considered a sequential solution of a series of coupled TDSE (which are the result of the projection above), each describing the evolution of any particular site tensor. Except in the special case that all these evolutions describe exactly the same dynamics (due to the state having maximal bond dimension), there is a finite time-step error of order $O(\delta^3)$ per time step and order $O(\delta^2)$ per unit time. In practice, the prefactor of this error is often much smaller than, e.g., in a TEBD calculation, in particular if the bond dimension of the input state is reasonably large. If the bond dimension is very small, the time-step error will be relatively large.

Third, the 2TDVP contains a SVD to split the evolved two-site tensor into two separate tensors again. During this SVD, a truncation is typically unavoidable, leading to a measurable truncation error. Careful analysis of this truncation error is necessary as always, but also proceeds in much the same way as always. In 1TDVP, this error is exactly zero.

The fourth source of error lies in the inexact solution of the local equations. Using sufficiently many Krylov vectors locally, it is very easy to make this error small. Therefore, one should always use sufficiently many vectors such that the obtained error is at least smaller than the truncation error in the previous step.

Note that changing the time-step size $\delta$ in the TDVP affects the four errors differently: the projection and truncation error affect each time step relatively independently of the size of that step. Hence, increasing the number of time steps during a fixed total time evolution increases the projection and truncation errors. The finite time-step error and the error from the inexact local solution, on the other hand, decrease when increasing the number of time steps and the total time is kept fixed. As such, choosing a smaller $\delta$ decreases the time-step error but increases the projection and truncation error. It is hence typically useful to take some care when choosing, e.g., the truncation threshold and the time-step size such as to approximately balance the induced errors.

Additionally, the energy and norm of the state are conserved exactly within the 1TDVP and only affected by the truncation error in the 2TDVP. This exact conservation may extend to those quantities which are contained within the Hamiltonian[4][5]. While such energy conservation is certainly very helpful to obtain long-time hydrodynamic observables such as diffusion constants, care has to be taken when using only 1TDVP during the calculation as shown in Ref. 6. Specifically, one has to take great care to ensure that the obtained data is completely converged in the bond dimension of the state at all times.


In practice, the 1/2TDVP method is quite similar to the 1/2DMRG method without subspace expansion or density matrix perturbation and nearly identical to the local Krylov method. Compared to the DMRG method, one of course has to replace the local eigensolver by a local exponentiation. Compared to both the local Krylov and the DMRG methods, we also need an additional backwards evolution step either on each bond (1TDVP) or the second site of a two-site evolution (2TDVP). This replacement of the ad-hoc basis transformation done by the local Krylov method with a properly motivated backwards evolution will result in smaller errors in each step.

Common Helper Functions

First, let us define some common helper functions. These are identical to those used in standard DMRG ground-state search algorithms. In addition, we need a local exponential Lanczos solver. For a more detailed description of solver used to evaluate the local exponentials, see the page on the global Krylov method. 1TDVP and 2TDVP then only differ in which SWEEP-LEFT and SWEEP-RIGHT functions are called from within the TIMESTEP function. [NB: The CONTRACT-LEFT and CONTRACT-RIGHT functions defined here are identical to those on the local Krylov page. Once page includes are available, merge them]

\begin{align} & \texttt{CONTRACT-LEFT}(L_{j-1}, W_j, A_j) \; \{ \\ & \quad L_{j; m_j}^{\bar{m}_j, w_j} = \sum_{ \sigma_{j}, \sigma^\prime_{j}, w_{j-1}, m_{j-1}, \bar{m}_{j-1} } L_{j-1; m_{j-1}}^{\bar{m}_{j-1}, w_{j-1}} \bar{A}^{\sigma_{j}}_{j;\bar{m}_{j-1}, \bar{m}_{j}} W_{j; w_{j-1}, w_{j}}^{\sigma_{j}, \sigma^\prime_{j}} A^{\sigma^\prime_{j}}_{j; m_{j-1}, m_{j}} \\ & \} \\ & \nonumber \\ & \texttt{CONTRACT-RIGHT}(R_{j+1}, W_j, B_j) \; \{ \\ & \quad R_{j; m_{j-1}}^{\bar{m}_{j-1}, w_{j-1}} = \sum_{ \sigma_{j}, \sigma^\prime_{j}, w_j, m_j, \bar{m}_j } \bar{B}^{\sigma_{j}}_{\bar{m}_{j-1}, \bar{m}_{j}} W_{j; w_{j-1}, w_{j}}^{\sigma_{j}, \sigma^\prime_{j}} B^{\sigma^\prime_{j}}_{j; m_{j-1}, m_{j}} R_{j+1; m_j}^{\bar{m}_j, w_j} \\ & \} \\ & \nonumber \\ & \texttt{INITIALISE}(\textrm{MPO } \{ W_j \}_{j=1}^L, \textrm{MPS } \{ M_j \}_{j=1}^L) \; \{ \\ & \quad L_{0;m_0}^{\bar{m}_0, w_0} \gets 1 \textrm{ and } R_{L+1;m_L}^{\bar{m}_L,w_L} \gets 1 \\ & \quad \textrm{Right-normalize } \{ M_j \}_{j=1}^L \to \{ B_j \}_{j=1}^L \textrm{ from right to left } \\ & \quad \textbf{for } j \in [L, 2] \{ \\ & \quad \quad R_j \gets \texttt{CONTRACT-RIGHT}(R_{j+1}, W_j, B_j) \\ & \quad \} \\ & \quad \textbf{return } L_0, \; \{ R_j \}_{j=2}^{L+1}, \; \{ B_j \}_{j=1}^L \\ & \} \\ & \nonumber \\ & \texttt{TIMESTEP}(\delta, \; L_0, \; \{ R_j \}_{j=2}^{L+1}, \; \{ W_j \}, \; \{ M_1, \; B_j \}_{j=2}^L) \; \{ \\ & \quad \{ L_j \}_{j=0}^{L-1}, \; \{ A_j, M_L \}_{j=1}^{L-1} \gets \texttt{SWEEP-RIGHT}(\frac{\delta}{2}, \; L_0, \; \{ R_j \}_{j=2}^L, \; \{ W_j \}_{j=1}^L, \; \{ M_1, B_j \}_{j=2}^L) \\ & \quad \{ R_j \}_{j=2}^{L+1}, \; \{ M_1, B_j \}_{j=2}^L \gets \texttt{SWEEP-LEFT}(\frac{\delta}{2}, \; R_{L+1}, \; \{ L_j \}_{j=0}^{L-1}, \; \{ W_j \}_{j=1}^L, \; \{ A_j, M_L \}_{j=1}^{L-1}) \\ & \} \\ \end{align}


\begin{align} & \texttt{SWEEP-RIGHT}(\delta, \; L_0, \; \{ R_j \}_{j=2}^{L+1}, \; \{ W_j \}_{j=1}^L, \; \{ M_1, \; B_j \}_{j=2}^L) \; \{ \\ & \quad \textbf{for } j \in [1, L] \\ & \quad \quad M^{\vphantom{\mathrm{eff}}}_j \gets \mathrm{exp}(-\mathrm{i}\frac{\delta}{2} \hat H^{\mathrm{eff}}_j) M^{\vphantom{\mathrm{eff}}}_j \textrm{ using } \hat H_j^{\mathrm{eff}} \equiv L^{\vphantom{\mathrm{eff}}}_{j-1} \cdot W^{\vphantom{\mathrm{eff}}}_j \cdot R^{\vphantom{\mathrm{eff}}}_{j+1} \\ & \quad \quad A_j, C_{\underline{j}} \gets M_j \textrm{ via QR decomposition} \\ & \quad \quad L_j \gets \texttt{CONTRACT-LEFT}(L_{j-1}, \; W_j, \; A_j) \\ & \quad \quad \textbf{if }j \neq L \\ & \quad \quad \quad C^{\vphantom{\mathrm{eff}}}_{\underline{j}} \gets \mathrm{exp}(\mathrm{i}\frac{\delta}{2} \hat H_{\underline{j}}^{\mathrm{eff}}) C^{\vphantom{\mathrm{eff}}}_{\underline{j}} \textrm{ using } \hat H_{\underline{j}}^{\mathrm{eff}} \equiv L^{\vphantom{\mathrm{eff}}}_{j} \cdot R^{\vphantom{\mathrm{eff}}}_{j+1} \\ & \quad \quad \quad M_{j+1} \gets C_{\underline{j}} \cdot B_{j+1} \\ & \quad \quad \quad \textrm{Delete } R_{i+1} \\ & \quad \quad \} \\ & \quad \} \\ & \quad \textbf{return } \{ L_j \}_{j=0}^{L-1}, \; \{ A_j, \; M_L \}_{j=1}^{L-1} \\ & \} \\ & \nonumber \\ & \texttt{SWEEP-LEFT}(\delta, \; R_{L+1}, \; \{ L_j \}_{j=0}^{L-1}, \; \{ W_j \}_{j=1}^L, \; \{ A_j, \; M_L \}_{j=1}^{L-1}) \; \{ \\ & \quad \textbf{for } j \in [L, 1] \\ & \quad \quad M^{\vphantom{\mathrm{eff}}}_j \gets \mathrm{exp}(-\mathrm{i}\frac{\delta}{2} \hat H^{\mathrm{eff}}_j) M^{\vphantom{\mathrm{eff}}}_j using \hat H^{\mathrm{eff}}_j \equiv L^{\vphantom{\mathrm{eff}}}_{j-1} \cdot W^{\vphantom{\mathrm{eff}}}_j \cdot R^{\vphantom{\mathrm{eff}}}_{j+1} \\ & \quad \quad B_j, C_{\underline{j-1}} \gets M_j \textrm{ via QR decomposition} \\ & \quad \quad R_j \gets \texttt{CONTRACT-RIGHT}{R_{j+1}, W_j, B_j} \\ & \quad \quad\textbf{if }{i \neq 1} \\ & \quad \quad \quad C^{\vphantom{\mathrm{eff}}}_{\underline{j-1}} \gets \mathrm{exp}(\mathrm{i}\frac{\delta}{2} \hat H^{\mathrm{eff}}_{\underline{j-1}}) C^{\vphantom{\mathrm{eff}}}_{\underline{j-1}} \textrm{ using } \hat H^{\mathrm{eff}}_{\underline{j-1}} \equiv L^{\vphantom{\mathrm{eff}}}_{j-1} \cdot R^{\vphantom{\mathrm{eff}}}_{j} \\ & \quad \quad \quad A_{j-1} \gets A_{j-1} \cdot C_{\underline{j-1}} \\ & \quad \quad \quad \textrm{Delete }L_{j-1} \\ & \quad \quad \} \\ & \quad \} \\ & \quad \textbf{return } \{ R_j \}_{j=2}^{L+1}, \; \{ M_1, B_j \}_{j=2}^L \\ & \} \end{align}


\begin{align} & \texttt{SWEEP-RIGHT}(\delta, \; L_0, \; \{ R_j \}_{j=3}^{L+1}, \; \{ W_j \}_{j=1}^L, \; \{ M_1, B_j \}_{j=2}^L) \\ & \quad \textbf{for }{j \in [1, L-1]} \\ & \quad \quad T_{j,j+1} \gets \sum_{m_j} M_{j;m_{j-1}, m_j}^{\sigma_i} B_{j+1;m_j, m_{j+1}}^{\sigma_{j+1}} \\ & \quad \quad T^{\vphantom{\mathrm{eff}}}_{j,j+1} \gets \mathrm{exp}(-\mathrm{i}\frac{\delta}{2} \hat H^{\mathrm{eff}}_{(j,j+1)}) T^{\vphantom{\mathrm{eff}}}_{j,j+1} \textrm{ using } \hat H^{\mathrm{eff}}_{(j,j+1)} \equiv L^{\vphantom{\mathrm{eff}}}_{j-1} \cdot W^{\vphantom{\mathrm{eff}}}_j \cdot W^{\vphantom{\mathrm{eff}}}_{j+1} \cdot R^{\vphantom{\mathrm{eff}}}_{j+2} \\ & \quad \quad A_j, C_{\underline{j}}, B_{j+1} \gets T_{j,j+1} \textrm{ via singular value decomposition and truncation } \\ & \quad \quad M_{j+1} \gets C_{\underline{j}} \cdot B_{j+1} \\ & \quad \quad \textbf{if }{j \neq L-1} \\ & \quad \quad \quad L_j \gets \texttt{CONTRACT-LEFT}(L_{j-1}, W_j, A_j) \\ & \quad \quad \quad M^{\vphantom{\mathrm{eff}}}_{j+1} \gets \mathrm{exp}(\mathrm{i}\frac{\delta}{2} \hat H^{\mathrm{eff}}_{j+1}) M^{\vphantom{\mathrm{eff}}}_{j+1} \textrm{ using } \hat H_{j+1}^{\mathrm{eff}} \equiv L^{\vphantom{\mathrm{eff}}}_{j} \cdot W^{\vphantom{\mathrm{eff}}}_{j+1} \cdot R^{\vphantom{\mathrm{eff}}}_{j+2} \\ & \quad \quad \quad \textrm{Delete } R_{j+2} \\ & \quad \quad \} \\ & \quad \} \\ & \quad \textbf{return } \{ L_j \}_{j=0}^{L-2}, \{ A_j, M_L \}_{j=1}^{L-1} \\ & \} \\ & \nonumber \\ & \texttt{SWEEP-LEFT}(\delta, R_{L+1}, \{ L_j \}_{j=0}^{L-2}, \{ W_j \}_{j=1}^L, \{ A_j, M_L \}_{j=1}^{L-1}) \\ & \quad \textbf{for }{j \in [L, 2]} \\ & \quad \quad T_{j-1,j} \gets \sum_{m_{j-1}} A_{j-1;m_{j-2}, m_{j-1}}^{\sigma_{j-1}} M_{j;m_{j-1}, m_{i}}^{\sigma_{i}} \\ & \quad \quad T^{\vphantom{\mathrm{eff}}}_{j-1,j} \gets \mathrm{exp}(-\mathrm{i}\frac{\delta}{2} \hat H^{\mathrm{eff}}_{(j-1,j)}) T^{\vphantom{\mathrm{eff}}}_{j-1,j} \textrm{ using } \hat H^{\mathrm{eff}}_{(j-1,j)} \equiv L^{\vphantom{\mathrm{eff}}}_{j-2} \cdot W^{\vphantom{\mathrm{eff}}}_{j-1} \cdot W^{\vphantom{\mathrm{eff}}}_{j} \cdot R^{\vphantom{\mathrm{eff}}}_{j+1} \\ & \quad \quad A_{j-1}, C_{\underline{j-1}}, B_{j} \gets T_{j-1,j} \textrm{ via singular value decomposition and truncation} \\ & \quad \quad M_{j-1} \gets A_{j-1} \cdot C_{\underline{j-1}} \\ & \quad \quad \textbf{if }{j \neq 2} \\ & \quad \quad \quad R_j \gets \texttt{CONTRACT-RIGHT}{R_{j+1}, W_j, B_j} \\ & \quad \quad \quad M^{\vphantom{\mathrm{eff}}}_{j-1} \gets \mathrm{exp}(\mathrm{i}\frac{\delta}{2} \hat H^{\mathrm{eff}}_{j-1}) M^{\vphantom{\mathrm{eff}}}_{j-1} using \hat H_{j-1}^{\mathrm{eff}} \equiv L^{\vphantom{\mathrm{eff}}}_{j-2} \cdot W^{\vphantom{\mathrm{eff}}}_{j-1} \cdot R^{\vphantom{\mathrm{eff}}}_{j} \\ & \quad \quad \quad \textrm{Delete } L_{j-2} \\ & \quad \quad \} \\ & \quad \} \\ & \quad \textbf{return } \{ R_j \}_{j=2}^{L+1}, \{ M_1, B_j \}_{j=2}^{L} \\ & \} \end{align}

The content of this page is based on Time-evolution methods for matrix-product states by S. Paeckel, T. Köhler, A. Swoboda, S. R. Manmana, U. Schollwöck and C. Hubig and is licensed under the CC-BY 4.0 license.


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