# pyCTQW.MPI.Graph2P¶

class Graph2P(N, filename=None, filetype=None, d=None, amp=None, interaction=0.0, bosonic=False)

Performs and analyses 2 particle continuous-time quantum walks on graphs

Parameters: N (int) – number of nodes to initialize the walker with. Nodes are labeled $$j\in\{0,N-1\}$$.

Example

To create a 2P CTQW Graph object for a 10 node graph,

>>> walk = pyCTQW.MPI.Graph2P(10)


Note

if filename AND filetype are provided, this automatically creates a PETSc Hamiltonian matrix, neglecting the need to run createH(). For details on the other keyword arguments, see createH().

## Method Summary¶

 Graph2P.clearLiveGraph() Destroys the live plot object previously created by Graph2P.plotLiveGraph(). Graph2P.createH(filename, filetype[, d, ...]) Generate the Hamiltonian of the graph. Graph2P.createInitState(initState) Generate the initial state of the quantum walk. Graph2P.destroy() Destroys the 2 particle quantum walk object, and all associated PETSc matrices/vectors. Graph2P.exportPartialTrace(filename, filetype) Exports the partial trace of the density matrix to a file. Graph2P.exportState(filename, filetype) Exports the final state psi of the quantum walk to a file. Graph2P.importInitState(filename, filetype) Imports the initial state of the quantum walk from a file. Graph2P.plot(filename) Creates a plot of probability vs node. Graph2P.plotEntanglement(filename) Creates a plot of the entanglement over time. Graph2P.plotGraph([size, probX, probY, output]) Creates a plot of probability vs node superimposed on a 3D visualisation of the graph vertices. Graph2P.plotLiveGraph(dt[, size, probX, probY]) Creates a live, updated plot of probability vs node superimposed on a 3D visualisation of the graph vertices. Graph2P.plotNode(filename, node[, t]) Creates a plot of the marginal probablities on a specified node over time. Graph2P.plotNodes(filename[, p, t]) Creates a plot of the node probablities over time. Graph2P.propagate(t[, method]) Propagates the quantum walk for time t. Graph2P.psiToInit() Copies the state self.psi to self.psi0. Graph2P.watch(nodes[, watchtype]) Creates a handle that watches either marginal probability or walker entanglement during propagation.

## Attribute Summary¶

 Attribute Type Description Graph2P.psi0 petsc4py.PETSc.Vec() $$N^2$$ element PETSc vector containing the initial state Graph2P.psi petsc4py.PETSc.Vec() $$N^2$$ element PETSc vector containing the final state after the last propagation. Graph2P.psiX petsc4py.PETSc.Vec() $$N$$ element PETSc vector containing the marginal propability of particle 1 Graph2P.psiY petsc4py.PETSc.Vec() $$N$$ element PETSc vector containing the marginal propability of particle 2 Graph2P.H pyCTQW.MPI.ctqw.Hamiltonian() Hamiltonian matrix Graph2P.EigSolver pyCTQW.MPI.ctqw.EigSolver() The Hamiltonian eigensolver Graph2P.handle pyCTQW.MPI.ctqw.nodeHandle() A node handle, created if nodes are being watched for probability. Graph2P.entanglementHandle pyCTQW.MPI.ctqw.entanglementHandle() A node handle, created if the entanglement is being watched.