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# Krylov Time Evolution (Global Krylov)

$\newcommand{\braket}{ \langle #1 \rangle }$

Krylov subspace methods (e.g. the Lanczos method) are well-known iterative techniques from the field of numerical linear algebra. In their application to time-dependent problems, one approximates the action of $\hat U^\mathrm{exact}(\delta)$ onto the quantum state $\ket{\psi(t)}$ directly, resulting in a time-evolved state $\ket{\psi(t+\delta)}$. It does not provide access to the exponential $e^{-\mathrm{i} \delta \hat H}$ in the standard physical basis. The most straight-forward approach is to ignore the special structure of the MPS/MPO representation and directly implement the iterative procedure, as detailed below. This is what we call the \textit{global Krylov method}. In contrast, a variant exploiting the structure of the MPS ansatz is called the local Krylov method and identical to the time-step targeting method of sytem-environment DMRG.

Here, we first introduce the Krylov method independent of the specific representation (dense vectors as in exact diagonalization, MPS, tree tensor networks etc.) used. This algorithm is also used as the local integrator for the local Krylov method and the TDVP. Subsequently, we will discuss particular caveats when applying the method globally to matrix-product states.

The Krylov subspace $\mathcal{K}_N$ of a Hamiltonian $\hat H$ and initial state $\ket{\psi}$ is defined as the span of vectors $\{ \ket{\psi}, \hat H \ket{\psi}, \ldots, \hat H^{N-1} \ket{\psi} \}$. This space is spanned by the Krylov vectors $\ket{v_0}$, $\ket{v_1}$, $\ldots$, $\ket{v_{N-1}}$ such that the first Krylov vector $|v_0\rangle$ is set to $\ket{\psi}$ normalized to have norm $1$, and the subsequent Krylov vectors $\ket{v_i}$ are constructed by applying $\hat H$ to $\ket{v_{i-1}}$ and orthonormalizing against all previous Krylov vectors equivalently to the Lanczos algorithm. In exact arithmetics with Hermitian $\hat H$, this way to construct a Krylov subspace reduces to orthogonalizing against the previous two vectors $\ket{v_{i-1}}$ and $\ket{v_{i-2}}$, which is equivalent to the Lanczos algorithm .

However, due to round-off errors intrinsic to a numerical implementation, orthogonality of the Krylov vectors is usually lost. If the precision required of each solution is low, one may abstain from avoiding this problem and simply work in a very small subspace. However, due to the accumulation of errors during a time evolution and the calculation of spectral or time-dependent properties, it is necessary to cure this problem. Hence, one typically needs to explicitly orthogonalize each new Krylov vector against all previous Krylov vectors. (Alternatively, one may only orthogonalize against the previous two vectors and achieve complete orthogonality by a subsequent basis transformation as detailed in Ref. 5.) The method then proceeds by searching for the element of $\mathcal{K}_N$ which approximates the result of the exact evolution most closely:

\begin{equation} \hat U^\mathrm{exact}(\delta)\ket{\psi(t)} \approx \mathrm{argmin}_{\ket{u} \in \mathcal{K}_N}\| \ket{u} - \hat U^\mathrm{exact}(\delta)\ket{\psi(t)} \|\equiv \ket{\psi_N(t + \delta)} \;. \end{equation}

To do so, we define the projector onto $\mathcal{K}_N$

\begin{align} \hat P_N & = \sum_{i=0}^{N-1} \ket{v_{i}}\langle v_i | \\ & = \begin{pmatrix} \Big|\begin{matrix} \vdots \\ v_0 \\ \vdots \end{matrix}\Big\rangle & \Big|\begin{matrix} \vdots \\ v_{1} \\ \vdots \end{matrix}\Big\rangle & \cdots & \Big|\begin{matrix} \vdots \\ v_{N-1} \\ \vdots \end{matrix}\Big\rangle \end{pmatrix} \cdot \begin{pmatrix} \bra{\cdots v_{0\hspace{0.5cm}\hbox{}} \cdots} \\ \bra{\cdots v_{1\hspace{0.5cm}\hbox{}} \cdots} \\ \vdots\\ \bra{\cdots v_{N-1} \cdots} \end{pmatrix} \equiv V_N^\dagger V_N \end{align}

where we have introduced matrices $V_N$ and $V_N^\dagger$ to represent the maps from the Hilbert space onto the Krylov space and vice versa. The solution to the above minimization problem is given by

\begin{equation} \ket{\psi_N(t + \delta)} = \hat P_N^\dagger \hat U^\mathrm{exact}(\delta) \hat P_N \ket{\psi(t)} \;. \end{equation}

Note that for $N = {\rm dim} \mathcal{H} \equiv N_{\mathcal{H}}$ this is exact. Expanding the projectors and writing down the formal Taylor series for $\hat U^\mathrm{exact}(\delta)$, we find:

\begin{align} |\psi_{N_{\mathcal{H}}}(t + \delta)\rangle & = \sum_{i=0}^{N_{\mathcal{H}}-1} \ket{v_{i}}\langle v_i | e^{-\mathrm{i} \delta \hat H} \sum_{i^\prime=0}^{N_{\mathcal{H}}-1} \ket{v_{i^\prime}}\braket{v_{i^\prime} \vert \psi(t)} \\ & = \sum_{i=0}^{N_{\mathcal{H}}-1} \ket{v_{i}}\langle v_i | \sum_{n=0}^\infty \frac{\left(-\mathrm{i} \delta\right)^n}{n!} \hat H^n \sum_{i^\prime=0}^{N_{\mathcal{H}}-1} \ket{v_{i^\prime}}\braket{v_{i^\prime} \vert \psi(t)} \\ & = V^\dagger_{N_{\mathcal{H}}} \sum_{n=0}^\infty \frac{\left(-\mathrm{i} \delta\right)^n}{n!} \underbrace{V_{N_{\mathcal{H}}} \hat H^n V^\dagger_{N_{\mathcal{H}}}}_{\equiv \left(T_{N_{\mathcal{H}}}\right)^n} V_{N_{\mathcal{H}}} \ket{\psi(t)} \\ & \approx V^\dagger_N \sum_{n=0}^\infty \frac{\left(-\mathrm{i} \delta\right)^n}{n!} \left(T_N\right)^n V_N \ket{\psi(t)} = V^\dagger_N e^{-\mathrm{i} \delta T_N} V_N \ket{\psi(t)} \label{eq:krylov:final} \;, \end{align}

where $N \ll N_{{\mathcal{H}}}$ and $T_N$ is the Krylov-space representation of $\hat H$ with coefficients

\begin{equation} \left(T_N \right)_{i,i^\prime} = \bra{v_i}\hat H\ket{v_{i^\prime}} \;. \end{equation}

The Krylov approximation is introduced in $\eqref{eq:krylov:final}$. Note that for $n > N-1$ \begin{equation} V_{N_{\mathcal{H}}} \hat H^n V_{N_{\mathcal{H}}}^\dagger V_{N_{\mathcal{H}}}\ket{\psi(t)} \neq \left(T_N\right)^n V_N\ket{\psi(t)}\;. \end{equation}

This implies that the error in the Taylor-expansion of the time- evolution operator is of order $\frac{\delta^n}{n!}$, indicating that already a few iterations suffice to give a very small error in the integrator. If $V_N^\dagger V_N$ were a proper identity, we could insert it in between any power of $\hat H$ and obtain exactly $V_N \hat H^n V_N^\dagger = T_N^n$. However, our Krylov subspace is much smaller than the true Hilbert space and the projection induced by $V_N^\dagger V_N$ is hence very large, $V_N^\dagger V_N \neq \mathbf{1}$. But due to the special structure of this Krylov space, $V_N^\dagger T_N^n V_N\ket{\psi(t)}$ converges much more quickly to $\hat H^n\ket{\psi(t)}$ than expected if we, e.g., selected random vectors in the full Hilbert space to construct our subspace.

In exact arithmetic and with Hermitian $\hat H$, $T_N$ would be tridiagonal, i.e., $\left(T_N\right)_{i,i^\prime} = 0$ if $|i-i^\prime| > 1$. While in practice this is not necessarily true, we found that it improves numerical stability and accuracy of the results to assume $T_N$ to be tridiagonal and only evaluate those elements while forcibly setting all other elements to zero. Returning to our equation $\eqref{eq:krylov:final}$, $V_N \ket{\psi(t)}$ is the Krylov-space vector

\begin{equation} (\| \ket{\psi(t)} \|, 0, 0, \ldots)^T \end{equation}

as all other Krylov vectors are orthogonal to $\ket{v_0}$ and $\ket{v_0}$ is the normalized version of $\ket{\psi(t)}$. $T_N$ can be exponentiated efficiently using standard diagonalization routines from LAPACK, as it is only of size $N \times N$. With $T_N = Q_N^\dagger D_N Q_N$ this yields

\begin{equation} e^{-\mathrm{i} \delta T_N} = Q^\dagger_N e^{-\mathrm{i} \delta D_N} Q_N \;. \end{equation}

For a given number of Krylov vectors and step size $\delta$, we hence obtain

\begin{align} \ket{\psi_N(t + \delta)} & = \| \ket{\psi(t)}\| V^\dagger_N Q^\dagger_N e^{-\mathrm{i} \delta D_N} Q_N e^1_N \\ & = \| \ket{\psi(t)}\| V^\dagger_N c_N \end{align}

with the coefficient vector $c_N = Q^\dagger_N e^{-\mathrm{i} \delta D_N} Q_N e^1_N$ and $e^1_N$ the $N$-dimensional unit vector $(1, 0, 0, \ldots)^T$. For typical problems as presented in the example section, the number of Krylov vectors used by us was between $3$ and $10$.

## Algorithm

The main input of the Krylvo method are the Hamiltonian $\hat H$, the initial state $\ket{\psi(0)}$ and the (possibly complex) time step. Additionally, a procedure APPLY-ORTHONORMALIZE is needed, which in turn requires the operator-state product and the orthogonalization of states. For details on a variational approach to this, see Ref. 9, Sec. 2.8.2. The function COMPUTE-EFFECTIVE-H only needs to update the new elements of $T_{j+1}$ compared to $T_j$.

### Evaluation of expectation values $\braket{\hat O(t+\delta)}$

It is not actually necessary to construct the time-evolved state $\ket{\psi_N(t+\delta)}$ to evaluate $\braket{\hat O(t+\delta)}$ for arbitrary $\delta$. Instead, evaluating $\bra{v_i} \hat O \ket{v_{i^\prime}}$ for all $i,i^\prime=[0, \ldots, N-1]$ and constructing only the coefficient vector $c_N$ is sufficient to evaluate the observable. One can hence potentially avoid adding up the Krylov vectors and constructing the time-evolved state $\ket{\psi(t+\delta)}$. Indeed, by evaluating the expectation values $\bra{v_i} \hat O \ket{v_{i^\prime}}$, it becomes possible to calculate the value of the observable at any time $\delta^\prime < \delta$ by only operating on small $N \times N$ matrices. Hence very small time steps unattainable by the other time-stepping methods (e.g. $\delta^\prime = 10^{-5}$) become available.

## Errors

Elaborate bounds are available to estimate the error incurred during the Krylov approximation. Unfortunately, these bounds are proven under the assumption of exact arithmetic and hence do not necessarily apply in the context of matrix-product states. The main take-away, which is confirmed in practice, is that the Krylov error is in $O( \delta^N )$ as long as $\sqrt{W \delta} \leq N$ where $W$ is the spectral range, which, in turn, is roughly of the same order of magnitude as the system size. For a typical system of $L=100$ sites with $\delta = 0.1$, this condition is fulfilled as soon as $N \approx 3$; more precise bounds are available in exact arithmetic. In exact arithmetic for a spectral width $W$ of $\hat H$, the error is smaller than $10 e^{-\frac{N^2}{(5 W \delta)}}$ if $N$ is between $\sqrt{4 W \delta}$ and $2 W \delta$ and the error is smaller than $\left(\frac{10}{W \delta}\right) e^{-W \delta} \left( \frac{e W \delta}{N} \right)^N$ if $N > 2 W \delta$ . It is unknown how this translates to the case of inexact arithmetic.

Hence, there are two approaches to measure the convergence of the Krylov space: (i) The bottom-rightmost entry of the effective matrix $T_n$ measures the weight scattered out of the Krylov space by application of the Hamiltonian and typically decays exponentially; (ii) the full Hilbert-space 2-norm distance between two sequential iterations is cheaply available through the coefficients of Krylov vectors produced in the two iterations. In our experience, this second measure makes for an excellent convergence criterion.

In addition to the inherent Krylov error which can often be made extremely small ($O(10^{-10})$ or smaller), the Krylov method of course also suffers from the standard MPS truncation error – this error, too, can be measured precisely (via the discarded weight) and be made very small. As such, both errors of the global Krylov method can be made extremely small at finite time-step size, albeit at relatively large numerical cost. The method hence in particular excels if used to evaluate states very precisely, e.g., to measure the errors of other methods on short time scales.

\subsection{Application to matrix-product states} Up to this point, there was no need to narrow the description down to a specific representation, which serves as a proof for the versatility of the Krylov method. In our practical calculations, however, we wish to use MPS to represent the time-evolved quantum states and intermediate Krylov vectors and an MPO to represent the Hamiltonian $\hat H$, which requires a few minor adaptations for efficiency and accuracy. Note that in stark contrast to the TEBD and MPO \wiii method, only an MPO representation of $\hat H$ and no analytical or other decomposition is required.

First and foremost, the most obvious improvement is in the calculation of the last entry of the effective Krylov matrix $T_N$. In exact or dense arithmetic, the evaluation of $\bra{v_{N-1}} \hat H \ket{v_{N-1}}$ requires computing the matrix-vector product $\hat H \ket{v_{N-1}}$. This is not the case in the MPS approach: Indeed, evaluating the expectation value $\bra{v_{N-1}} \hat H \ket{v_{N-1}}$ is much cheaper than calculating the MPS representing $\hat H \ket{v_{N-1}}$. As such, to generate a $N \times N$-dimensional effective Krylov matrix $T_N$, one only needs to evaluate $N-1$ MPO-MPS products and avoids the MPO-MPS product for the most costly application on the last Krylov vector. In our experience, the bond dimension of every additional Krylov vector grows superlinearly, making this optimization very worthwhile.

To construct the time-evolved state $\ket{\psi(t+\delta)}$, it is necessary to sum $N$ Krylov vectors together. Various methods to do so exist, in our implementation, we sequentially add two vectors (resulting in a new MPS with bond dimension $2m$) which are then truncated back down to the target bond dimension $m$. In $N-1$ steps, all $N$ Krylov vectors are summed together at cost $O(N (2m)^3)$. One could follow-up on this procedure with some sweeps of variational optimization or alternatively directly variationally optimize, but this does not appear to be necessary for our application.

## Loss of orthogonality

A typical problem of Krylov-subspace methods is the possible loss of orthogonality of the basis vectors due to the finite-precision arithmetic of floating point operations. This matter becomes substantially more pressing in the matrix-product algebra, as truncation is crucial to keeping computations feasible. If many Krylov vectors are desired, truncation errors affecting the orthogonality of the basis vectors do not simply add to the overall error (see above), but may quickly degrade the overall quality of the Krylov space, leading to a poor result. In this case, it is necessary to check for orthogonality in the basis and eventually re-orthogonalize the basis vectors successively. However, if one uses a simple Gram-Schmidt procedure to orthogonalize vectors by successive additions of MPS, new truncation errors are introduced during this procedure, which will quite often entail the same problem.

In our experience, it has proven fruitful to orthogonalize the new Krylov states variationally against all other Krylov states. This is essentially variational compression of a state under the additional constraints of having zero overlap with all previous Krylov states. The additional constraints can be incorporated with the method of Lagrange multipliers: For each constraint (orthogonal vector $\ket{v_i}$), introduce the respective Lagrange multiplier $\beta_i$. To minimize $\|\hat{H}\ket{v_i}-\ket{v_{i+1}}\|^2$ under the constraints $\{\braket{v_{i+1}|v_{i^\prime}} = 0\}_{0\leq i^\prime \leq i}$, we actually minimize

\begin{align} \left\|\hat{H}\,\ket{v_i}-\ket{v_{i+1}}\right\|^2 + \sum_{i^\prime}\,\beta_{i^\prime}\,\braket{v_{i+1}|v_{i^\prime}} \label{eq:krylov:ortho} \end{align}

with regard to $\ket{v_i}$ and the $\beta_{i^\prime}$. As with variational compression, this can also be solved by iteratively solving the local one- or two-site problems (without explicitly evaluating $\braket{\hat H^2}$). Care should be taken to ensure local orthogonality by using the pseudo-inverse of the Gram matrix as explained in Ref. 6. Using a two-site approach entails an additional truncation step after each local optimization step and implies again a loss of orthogonality. However, the two-site approach converges much better than the single-site approach towards the global optimum. In practice, we hence first do a few sweeps using the two-site optimization (or, similarly, a single-site optimization with subspace expansion) and follow up with a few sweeps of fully single-site optimization without expansion and hence also without truncation. The resulting state is then exactly orthogonal to all previous states. Note that when initially starting the optimization $\eqref{eq:krylov:ortho}$, the available vector space on the first few sites is likely to be very small (e.g., $\sigma^2\cdot (m_2 = \sigma^2)$) and the orthogonalization hence overconstrained. To avoid this problem, one should add the constraints one-by-one during subsequent sweeps.

This variational orthogonalization can either be used as a separate orthogonalization step after the MPO-MPS application (using any of the known algorithms) or it can be combined with the variational operator application. Whether it is better to first do the MPO-MPS application using the zip-up method and then variationally orthogonalize the result or to do both steps at once depends on the system at hand: in particular with long-range interactions, the variational approach may require more sweeps to converge whereas short-range interactions are dealt with very efficiently there.

## Dynamic step sizing

Dynamic step sizing is one of the most interesting and powerful features of this method and can be used in several ways. The idea is that a Krylov subspace, which was computed for some time step $\delta$, can be recycled for some other step length. It is possible to distinguish two cases: interpolation and extrapolation.

### Interpolation

In some applications, the time evolution needs to be performed on a very fine grid in time. The time-stepping methods would involve a single step for each point of the grid, which can quickly turn cumbersome or even impossible. On the other hand, if we have a Krylov subspace that we used to perform a large time step, it can be re-used to compute any intermediate smaller time step at the same or higher accuracy. This immediately follows from the construction of the Krylov space and the convergence criteria/assumptions made above. As the diagonalization of the effective Hamiltonian is already known, all we need to do is exponentiate the diagonal times the new time step, map back into the Krylov basis to get the coefficient vector, and compute the new MPS as a superposition of Krylov vectors. If one is only interested in expectation values of an observable $\hat{O}$, it is advantageous to compute its projection into the Krylov space via $\left(O_{N}\right)_{i,i^\prime}=\braket{\phi_i|\hat{O}|\phi_{i^\prime}}$ with complexity $\sim\mathcal{O}(n^2)$. With the coefficient vector $c_N$, the desired expectation value can be computed as $c_N^\dagger O_N c_N$, entirely skipping the more expensive superposition of Krylov states.

### Extrapolation

Albeit trickier to implement, extrapolation can significantly improve performance when used as a kind of automatic adaptive step sizing scheme. The idea is as follows: Given a Krylov space, it is also often possible to recycle it for larger step sizes, by only adding a small number of additional Krylov vectors (or none at all). It follows that the optimal Krylov subspace dimension minimizes the ratio of the time needed to compute its basis and the number of steps that it can be used for. As crude approximations of these quantities, we assume that the cost of any new Krylov vector grows exponentially, i.e. the ratio of the costs of successive vectors is fixed. Furthermore, we also assume that any new Krylov vector allows us as many additional time steps as the previous Krylov vector. We then continuously monitor the time needed to construct a new Krylov vector and the number of steps we are able to take with it. Once a decision has to be taken whether to extend the Krylov space or rebuild it from scratch, we use those values as estimates for our decision. In practice, this heuristic has proven to be quite reliable.

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