% vim: ft=tex \section{Implementation} This chapter discusses how the concepts introduced before are implemented into a simulator. Futher the infrastructure around the simulation and some tools are explained. The implementation is written as the \lstinline{python3} package \lstinline{pyqcs} \cite{pyqcs}. This allows users to quickly construct circuits, apply them to states and measure amplitudes. Full access to the state (including intermediate states) has been priorized over execution speed. To keep the simulation speed as high as possible under these constraints some parts are implemented in \lstinline{C}. \subsection{Dense State Vector Simulation} \subsubsection{Representation of Dense State Vectors} Recalling \eqref{eq:ci} any $n$-qbit state can be represented as a $2^n$ component vector in the integer state basis. This representation has some useful features when it comes to computations: \begin{itemize} \item{The projection on the integer states is trivial.} \item{For any qbit $j$ and $0 \le i \le 2^n-1$ the coefficient $c_i$ is part of the $\ket{1}_j$ amplitude iff $i \& (1 << j)$ and part of the $\ket{0}_j$ amplitude otherwise.} \item{For a qbit $j$ the coefficients $c_i$ and $c_{i \hat{ } (1 << j)}$ are the conjugated coefficients.} \end{itemize} Where $\hat{}$ is the binary XOR, $\&$ the binary AND and $<<$ the binary leftshift operator. While implementing the dense state vectors two key points were allowing a simple and readable way to use them and simple access to the states by users that want more information than an abstracted view could allow. To meet both requirements the states are implemented as Python objects providing abstract features such as normalization checking, checking for sufficient qbit number when applying a circuit, computing overlaps with other states, a stringify method and stored measurement results. To store the measurement results a NumPy \lstinline{int8} array \cite{numpy_array} is used; this is called the classical state. The Python states also have a NumPy \lstinline{cdouble} array that stores the quantum mechanical state. Using NumPy arrays has the advantage that access to the data is simple and safe while operations on the states can be implemented in \lstinline{C} \cite{numpy_ufunc} providing a considerable speedup. This quantum mechanical state is the component vector in integer basis therefore it has $2^n$ components. Storing those components is acceptable in a range from $1$ to $30$ qbits; above this range the state requires space in the order of $1 \mbox{ GiB}$ which is in the range of usual RAM sizes for personal computers. For higher qbit numbers moving to high performance computers and other simulators is necessary. \subsubsection{Gates} Gates on dense state vectors are implemented as NumPy Universal Functions (ufuncs) \cite{numpy_ufunc} mapping a classical and a quantum state to a new classical state, a new quantum state and a $64 \mbox{ bit}$ integer indicating what qbits have been measured. Using ufuncs has the great advantage that managing memory is done by NumPy and an application programmer just has to implement the logic of the function. Because ufuncs are written in \lstinline{C} they provide a considerable speedup compared to an implementation in Python. The logic of gates is usually easy to implement using the integer basis. The example below implements the Hadamard gate \ref{ref:singleqbitgates}: \lstinputlisting[title={Implementation of the Hadamard Gate in C}, language=C, firstline=153, lastline=178, breaklines=true]{../pyqcs/src/pyqcs/gates/implementations/basic_gates.c} A basic set of gates is implemented in PyQCS: \begin{itemize} \item{Hadamard $H$ gate.} \item{Pauli $X$ or \textit{NOT} gate.} \item{Pauli $Z$ gate.} \item{The $S$ phase gate.} \item{$Z$ rotation $R_\phi$ gate.} \item{Controlled $X$ gate: $CX$.} \item{Controlled $Z$ gate: $CZ$.} \item{The "measurement gate" $M$.} \end{itemize} To allow the implementation of possible hardware related gates the class \lstinline{GenericGate} takes a unitary $2\times2$ matrix as a NumPy \lstinline{cdouble} array and builds a gate from it. \subsubsection{Circuits} \label{ref:pyqcs_circuits} As mentioned in \ref{ref:quantum_circuits} quantum circuits are central in quantum programming. In the implementation great care was taken to make writing circuits as convenient and readable as possible. Users will almost never access the actual gates that perform the operation on a state; instead they will handle circuits.\\ Circuits can be applied to a state by multiplying them from the left on a state object: \begin{lstlisting}[language=Python] new_state = circuit * state \end{lstlisting} The elementary gates such as $H, R_\phi, CX$ are implemented as single gate circuits and can be constructing using the built-in generators. The generators take the act-qbit as first argument, parameters such as the control qbit or an angle as second argument: \begin{lstlisting}[language=Python, breaklines=true, caption={Using Single Gate Circuits}] In [1]: from pyqcs import CX, CZ, H, R, Z, X ...: from pyqcs import State ...: ...: state = State.new_zero_state(2) ...: intermediate_state = H(0) * state ...: ...: bell_state = CX(1, 0) * intermediate_state In [2]: bell_state Out[2]: (0.7071067811865476+0j)*|0b0> + (0.7071067811865476+0j)*|0b11> \end{lstlisting} Large circuits can be constructed using the binary OR operator \lstinline{|} in an analogy to the pipeline operator on many *NIX shells. As usual circuits are read from left to right similar to pipelines on *NIX shells: %\adjustbox{max width=\textwidth}{ \begin{lstlisting}[language=Python, breaklines=true, caption={Constructing Circuits Using \lstinline{|}}] In [1]: from pyqcs import CX, CZ, H, R, Z, X ...: from pyqcs import State ...: ...: state = State.new_zero_state(2) ...: ...: # This is the same as ...: # circuit = H(0) | CX(1, 0) ...: circuit = H(0) | H(1) | CZ(1, 0) | H(1) ...: ...: bell_state = circuit * state In [2]: bell_state Out[2]: (0.7071067811865477+0j)*|0b0> + (0.7071067811865477+0j)*|0b11> \end{lstlisting} %} A quick way to generate circuits programatically is to use the \lstinline{list_to_circuit} function: %\adjustbox{max width=\textwidth}{ \begin{lstlisting}[language=Python, breaklines=true, caption={Constructing Circuits Using Python Lists}] In [1]: from pyqcs import CX, CZ, H, R, Z, X ...: from pyqcs import State, list_to_circuit ...: ...: circuit_CX = list_to_circuit([CX(i, i-1) for i in range(1, 5)]) ...: ...: state = (H(0) | circuit_CX) * State.new_zero_state(5) In [2]: state Out[2]: (0.7071067811865476+0j)*|0b0> + (0.7071067811865476+0j)*|0b11111> \end{lstlisting} %} \subsection{Graphical State Simulation} \subsubsection{Graphical States} For the graphical state $(V, E, O)$ the list of vertices $V$ can be stored implicitly by demanding $V = \{0, ..., n - 1\}$. This leaves two components that have to be stored: The edges $E$ and the vertex operators $O$. Storing the vertex operators is done using a \lstinline{uint8_t} array. Every local Clifford operator is associated from $0$ to $24$, their order is \begin{equation} \begin{aligned} &\left(\begin{matrix}\frac{\sqrt{2}}{2} & \frac{\sqrt{2}}{2}\\\frac{\sqrt{2}}{2} & - \frac{\sqrt{2}}{2}\end{matrix}\right), \left(\begin{matrix}1 & 0\\0 & i\end{matrix}\right), \left(\begin{matrix}1 & 0\\0 & 1\end{matrix}\right), \left(\begin{matrix}\frac{\sqrt{2}}{2} & \frac{\sqrt{2}}{2}\\\frac{\sqrt{2} i}{2} & - \frac{\sqrt{2} i}{2}\end{matrix}\right), \\ &\left(\begin{matrix}\frac{\sqrt{2}}{2} & \frac{\sqrt{2} i}{2}\\\frac{\sqrt{2}}{2} & - \frac{\sqrt{2} i}{2}\end{matrix}\right), \left(\begin{matrix}1 & 0\\0 & -1\end{matrix}\right), \left(\begin{matrix}\frac{\sqrt{2}}{2} & \frac{\sqrt{2} i}{2}\\\frac{\sqrt{2} i}{2} & \frac{\sqrt{2}}{2}\end{matrix}\right), \left(\begin{matrix}\frac{\sqrt{2}}{2} & - \frac{\sqrt{2}}{2}\\\frac{\sqrt{2}}{2} & \frac{\sqrt{2}}{2}\end{matrix}\right), \\ &\left(\begin{matrix}1 & 0\\0 & - i\end{matrix}\right), \left(\begin{matrix}\frac{\sqrt{2}}{2} & - \frac{\sqrt{2}}{2}\\\frac{\sqrt{2} i}{2} & \frac{\sqrt{2} i}{2}\end{matrix}\right), \left(\begin{matrix}\frac{\sqrt{2}}{2} & - \frac{\sqrt{2} i}{2}\\\frac{\sqrt{2}}{2} & \frac{\sqrt{2} i}{2}\end{matrix}\right), \left(\begin{matrix}\frac{\sqrt{2}}{2} & - \frac{\sqrt{2} i}{2}\\\frac{\sqrt{2} i}{2} & - \frac{\sqrt{2}}{2}\end{matrix}\right), \\ &\left(\begin{matrix}\frac{1}{2} + \frac{i}{2} & \frac{1}{2} - \frac{i}{2}\\\frac{1}{2} - \frac{i}{2} & \frac{1}{2} + \frac{i}{2}\end{matrix}\right), \left(\begin{matrix}\frac{\sqrt{2}}{2} & \frac{\sqrt{2}}{2}\\- \frac{\sqrt{2}}{2} & \frac{\sqrt{2}}{2}\end{matrix}\right), \left(\begin{matrix}0 & 1\\1 & 0\end{matrix}\right), \left(\begin{matrix}\frac{\sqrt{2}}{2} & \frac{\sqrt{2}}{2}\\- \frac{\sqrt{2} i}{2} & \frac{\sqrt{2} i}{2}\end{matrix}\right), \\ &\left(\begin{matrix}0 & 1\\i & 0\end{matrix}\right), \left(\begin{matrix}\frac{1}{2} - \frac{i}{2} & \frac{1}{2} + \frac{i}{2}\\- \frac{1}{2} + \frac{i}{2} & \frac{1}{2} + \frac{i}{2}\end{matrix}\right), \left(\begin{matrix}0 & i\\1 & 0\end{matrix}\right), \left(\begin{matrix}\frac{\sqrt{2}}{2} & \frac{\sqrt{2} i}{2}\\- \frac{\sqrt{2} i}{2} & - \frac{\sqrt{2}}{2}\end{matrix}\right), \\ &\left(\begin{matrix}\frac{1}{2} - \frac{i}{2} & - \frac{1}{2} + \frac{i}{2}\\- \frac{1}{2} + \frac{i}{2} & - \frac{1}{2} + \frac{i}{2}\end{matrix}\right), \left(\begin{matrix}0 & -1\\1 & 0\end{matrix}\right), \left(\begin{matrix}\frac{\sqrt{2}}{2} & - \frac{\sqrt{2}}{2}\\- \frac{\sqrt{2} i}{2} & - \frac{\sqrt{2} i}{2}\end{matrix}\right), \left(\begin{matrix}\frac{1}{2} - \frac{i}{2} & \frac{i \left(-1 + i\right)}{2}\\- \frac{1}{2} + \frac{i}{2} & \frac{i \left(-1 + i\right)}{2}\end{matrix}\right). \end{aligned} \end{equation} The edges are stored in an adjacency matrix \begin{equation} A = (a_{i,j})_{i,j = 0, ..., n-1} \end{equation} \begin{equation} \begin{aligned} a_{i,j} = \left\{ \begin{array}{c} 1 \mbox{, if } \{i,j\} \in E\\ 0 \mbox{, if } \{i,j\} \notin E \end{array}\right. . \end{aligned} \end{equation} Recalling some operations on the graph as described in \ref{ref:dynamics_graph}, \ref{ref:meas_graph} or Lemma \ref{lemma:M_a} one sees that it is important to efficiently access and modify the neighbourhood of a vertex. To ensure good performance when accessing the neighbourhood while keeping the required memory low a linked list-array hybrid is used to store the adjacency matrix. For every vertex the neighbourhood is stored in a sorted linked list (which is a sparse representation of a column vector) and these adjacency lists are stored in a length $n$ array. Using this storage method all operations including searching and toggling edges inherite their time complexity from the sorted linked list. \subsubsection{Operations on Graphical States} Operations on Graphical States are divided into three classes: Local Clifford operations, the CZ operation and measurements. The graphical states are implemented in \lstinline{C} and are exported to python3 in the class \lstinline{RawGraphState}. This class has three main methods to implement the three classes of operations. \begin{description} \item[\hspace{-1em}]{\lstinline{RawGraphState.apply_C_L}\\ This method implements local clifford gates. It takes the qbit index and the index of the local Clifford operator (ranging form $0$ to $23$).} \item[\hspace{-1em}]{\lstinline{RawGraphState.apply_CZ}\\ Applies the $CZ$ gate to the state. The first argument is the act-qbit, the second the control qbit (note that this is just for consistency to the $CX$ gate).} \item[\hspace{-1em}]{\lstinline{RawGraphState.measure}\\ Using this method one can measure a qbit. It takes the qbit index as first argument and a floating point (double precision) random number as second argument. This random number is used to decide the measurement outcome in non-deterministic measurements. This method returns either $1$ or $0$ as a measurement result.} \end{description} Because this way of modifying the state is rather unconvenient and might lead to many errors the \lstinline{RawGraphState} is wrapped by the pure python class\\ \lstinline{pyqcs.graph.state.GraphState}. It allows the use of circuits as described in \ref{ref:pyqcs_circuits} and provides the method \lstinline{GraphState.to_naive_state} to convert the graphical state to a dense vector state. \subsubsection{Pure C Implementation} Because python tends to be rather slow and might not run on any architecture a pure \lstinline{C} implementation of the graphical simulator is also provided. It should be seen as a reference implementation that can be extended to the needs of the user. This implementation reads byte code from a file and executes it. The execution is always done in three steps: \begin{enumerate}[1] \item{Initializing the state according the the header of the bytecode file.} \item{Applying operations given by the bytecode to the state. This includes local Clifford gates, $CZ$ gates and measurements (the measurement outcome is ignored).} \item{Sampling the state according the the description given in the header of the byte code file and writing the sampling results to either a file or \lstinline{stdout}. } \end{enumerate} \subsection{Utilities} To make both using the simulators more convenient and to help with using them in as scientific or educational context several utilities have been written. This chapter explains some of them. \subsubsection{Sampling and Circuit Generation} The function \lstinline{pyqcs.sample} provides a simple way to sample from a state. Copies of the state are made when necessary and the results are returned in a \lstinline{collections.Counter} object. Several qbits can be sampled at once; they can be passed to the function either as an integer which will be interpreted as a bit mask and the least significant bit will be sampled first. When passing the qbits to sample as a list of integers the integers are interpreted as qbit indices and are measured in the order they appear. If the keyword argument \lstinline{keep_states} is \lstinline{True} the sampling function will include the resulting states in the result. At the moment this works for dense vectors only. Checking for equality on graphical states has yet to be implemented but has $NP$ computational hardness \cite{dahlberg_ea2019}. Writing circuits out by hand can be rather painful. The function\\ \lstinline{pyqcs.list_to_circuit} Converts a list of circuits to a circuit. This is particularly helpful in combination with python's \lstinline{listcomp}: \begin{lstlisting}[caption={Generating a Large Circuit Efficiently}] circuit_H = list_to_circuit([H(i) for i in range(nqbits)]) \end{lstlisting} The module \lstinline{pyqcs.util.random_circuits} provides the method described in \ref{ref:performance} to generate random circuits for both graphical and dense vector simulation. Using the module \lstinline{pyqcs.util.random_graphs} one can generate random graphical states which is more performant than using random circuits. The function \lstinline{pyqcs.util.to_circuit.graph_state_to_circuit} converts graphical states to circuits (mapping the $\ket{0b0..0}$ to this state). Using these circuits the graphical state can be copied or converted to a dense vector state. Further it is a way to optimize circuits and later run them on other simulators. Also the circuits can be exported to \lstinline{qcircuit} code (see below) which is a relatively readable way to represent graphical states. \subsubsection{Exporting and Flattening Circuits} Circuits can be drawn using the \LaTeX package \lstinline{qcircuit}; all circuits in this documents use \lstinline{qcircuit}. To visualize the circuits built using \lstinline{pyqcs} the function\\ \lstinline{pyqcs.util.to_diagram.circuit_to_diagram} can be used to generate \lstinline{qcircuit} code that can be used in \LaTeX documents or exported to PDFs directly. The diagrams produced by this function are not optimized and the diagrams can be unnecessary long. Usually this can be fixed easily by editing the produced code manually. The circuits constructed using the \lstinline{|} operator have a tree structure which is rather unconvenient when optimizing circuits or exporting them. The function \\ \lstinline{pyqcs.util.flatten.flatten} converts a circuit to a list of single gate circuits that can be analyzed or exported easily. \subsection{Performance} \label{ref:performance} To test the performance and compare it to the dense vector simulator the python module is used. Although the pure \lstinline{C} implementation has potential for better performance the python module is better comparable to the dense vector simulator which is a python module as well. For performance tests (and for tests against the dense vector simulator) random circuits are used. Length $m$ circuits are generated from the probability space \begin{equation} \Omega = \left(\{1, ..., 4n\} \otimes \{1, ..., n-1\} \otimes [0, 2\pi)\right)^{\otimes m} \end{equation} with the uniform distribution. The continous part $[0, 2\pi)$ is unused when generating random circuits for the graphical simulator; when generating random circuits for dense vector simulations this is the argument $\phi$ of the $R_\phi$ gate. For $m=1$ an outcome is mapped to a gate using \begin{equation} \begin{aligned} F(i, k, x) = \left\{\begin{array}{cc} X(i - 1) & \mbox{, if } i \le n \\ H(i - n - 1) & \mbox{, if } i \le 2n\\ S(i - 2n - 1) & \mbox{, if } i \le 3n\\ CZ(i - 3n - 1, k - 1) & \mbox{, if } k \le i - 3n - 1 \\ CZ(i - 3n - 1, k) & \mbox{, if } k > i - 3n - 1\\ \end{array}\right. . \end{aligned} \end{equation} This method provides equal probability for $X, H, S$ and $CZ$ gate. For the dense vector simulator $S$ can be replaced by $R_\phi$ with the parameter $x$. Using this method circuits are generated and applied both to graphical and dense vector states and the time required to execute the operations \cite{timeit} is measured. The resulting graph can be seen in Figure \ref{fig:scaling_qbits_linear} and Figure \ref{fig:scaling_qbits_log}. Note that in both cases the length of the circuits have been scaled linearely with the amount of qbits and the measured time was divided by the number of qbits: \begin{equation} \begin{aligned} L_{\mbox{circuit}} &= \alpha n \\ T_{\mbox{rescaled}} &= \frac{T_{\mbox{execution}}(L_{\mbox{circuit}})}{n}\\ \end{aligned} \end{equation} \begin{figure} \centering \includegraphics[width=\linewidth]{../performance/scaling_qbits_linear.png} \caption[Runtime Behaviour for Scaling Qbit Number]{Runtime Behaviour for Scaling Qbit Number} \label{fig:scaling_qbits_linear} \end{figure} \begin{figure} \centering \includegraphics[width=\linewidth]{../performance/scaling_qbits_log.png} \caption[Runtime Behaviour for Scaling Qbit Number (Logarithmic Scale)]{Runtime Behaviour for Scaling Qbit Number (Logarithmic Scale)} \label{fig:scaling_qbits_log} \end{figure} The reason for this scaling will be clear later; one can observe that the performance of the graphical simulator increases in some cases with growing number of qbits when the circuit length is constant. The code used to generate the data for these plots can be found in \ref{ref:code_benchmarks}. As described by \cite{andersbriegel2005} the graphical simulator is exponentially faster than the dense vector simulator. According to \cite{andersbriegel2005} it is considerably faster than a simulator using the straight forward approach simulating the stabilizer tableaux like CHP \cite{CHP} with an average runtime behaviour of $\mathcal{O}\left(n\log(n)\right)$ instead of $\mathcal{O}\left(n^2\right)$. One should be aware that the gate execution time (the time required to apply a gate to the state) highly depends on the state it is applied to. For the dense vector simulator and CHP this is not true: Gate execution time is constant for all gates and states. Because the graphical simulator has to toggle neighbourhoods the gate execution time of the $CZ$ gate varies greatly. The plot Figure \ref{fig:scaling_circuits_linear} shows the circuit execution time for two different numbers of qbits. One can observe three regimes: \begin{figure} \centering \includegraphics[width=\linewidth]{../performance/regimes/scaling_circuits_linear.png} \caption[Circuit Execution Time for Scaling Circuit Length]{Circuit Execution Time for Scaling Circuit Length} \label{fig:scaling_circuits_linear} \end{figure} \begin{description} \item[Low-Linear Regime] {Here the circuit execution time scales approximately linearely with the number of gates in the circuit (i.e. the $CZ$ gate execution time is approximately constant). } \item[Intermediate Regime]{The circuit execution time has a nonlinear dependence on the circuit length.} \item[High-Linear Regime]{This regime shows a linear dependence on the circuit length; the slope is higher than in the low-linear regime.} \end{description} \begin{figure} \centering \includegraphics[width=\linewidth]{../performance/regimes/scaling_circuits_measurements_linear.png} \caption[Circuit Execution Time for Scaling Circuit Length with Random Measurements]{Circuit Execution Time for Scaling Circuit Length with Random Measurements} \label{fig:scaling_circuits_measurements_linear} \end{figure} \begin{figure} \centering \includegraphics[width=\linewidth]{graphics/graph_low_linear_regime.png} \caption[Typical Graphical State in the Low-Linear Regime]{Typical Graphical State in the Low-Linear Regime} \label{fig:graph_low_linear_regime} \end{figure} \begin{figure} \centering \includegraphics[width=\linewidth]{graphics/graph_intermediate_regime_cut.png} \caption[Window of a Typical Graphical State in the Intermediate Regime]{Window of a Typical Graphical State in the Intermediate Regime} \label{fig:graph_intermediate_regime} \end{figure} \begin{figure} \centering \includegraphics[width=\linewidth]{graphics/graph_high_linear_regime_cut.png} \caption[Window of a Typical Graphical State in the High-Linear Regime]{Window of a Typical Graphical State in the High-Linear Regime} \label{fig:graph_high_linear_regime} \end{figure} These two regimes can be explained when considering the graphical states that typical live in these regimes. With increased circuit length the amount of edges increases which makes toggling neighbourhoods harder. Graphs from the low-linear, intermediate and high-linear regime can be seen in Figure \ref{fig:graph_low_linear_regime}, Figure \ref{fig:graph_intermediate_regime} and Figure \ref{fig:graph_high_linear_regime}. Due to the great amount of edges in the intermediate and high-linear regime the pictures show a window of the actual graph. The full images are in \ref{ref:complete_graphs}. Further the regimes are not clearly visibe for $n>30$ qbits so choosing smaller graphs is not possible. The code that was used to generate these images can be found in \ref{ref:code_example_graphs}. The Figure \ref{fig:scaling_circuits_measurements_linear} brings more substance to this interpretation. In this simulation the Pauli $X$ gate has been replaced by the measurement gate $M$, .i.e. in every gate drawn from the probability space a qbit is measured with probability $\frac{1}{4}$. As described in \cite{hein_eisert_briegel2008} the Schmidt measure for entropy is bounded from above by Pauli persistency, i.e. the minimal amount of Pauli measurements required to disentangle a state. This Pauli persistency is closely related to the amount (and structure of) vertices in the graph \cite{hein_eisert_briegel2008}. In particular Pauli measurements decrease the entanglement (and the amount of edges) in a state \cite{hein_eisert_briegel2008}\cite{li_chen_fisher2019}. The frequent measurements in the simulation therefore keeps the amount of edges low thus preventing a transition from the low linear regime to the intermediate regime. Because states with more qbits reach the intermediate regime at higher circuit lengths it is important to account for this virtual performance boost when comparing with other simulation methods. This explains why the circuit length in Figure \ref{fig:scaling_qbits_linear} had to be scaled with the qbit number. \subsection{Future Work} Although the simulator(s) are in a working state and have been tested there is still some work that can be done. A noise model helping to teach and analyze noisy execution is one particularly interesting piece of work. To allow a user to execute circuits on other machines, including both real hardware and simulators, a module that exports circuits to OpenQASM \cite{openqasm} seems useful. The current implementation of some graphical operations can be optimized. While clearing VOPs as described in \ref{ref:dynamics_graph} the neighbourhood of a vertex is toggled for every $L_a$ transformation. This is the most straight forward implementation but often the $L_a$ transformation is performed several times on the same vertex. The neighbourhood would have to be toggled either once or not at all depending on whether the number of $L_a$ transformations is odd or even. When toggling an edge the simulator uses a series of well tested basic linked list operations: Searching an element in the list, inserting an element into the list and deleting an element from the list. This is known to have no bugs but the performance could be increased by operating directly on the linked list. Some initial work to improve this behaviour is already done but does not work at the moment.