---
title: "qwalkr"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{qwalkr}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
`qwalkr` is a package for the analysis of the time evolution of quantum walks. The package consists mainly of methods for the numerical estimation of matrices that arise in this field of research. This vignette intends to give an overview of the core functionalities provided by `qwalkr`.
## Installation
You can install the stable version of `qwalkr` from CRAN:
```{r, eval=FALSE}
install.packages("qwalkr")
```
For the development version, you can install from Github like so:
```{r, eval=FALSE}
# install.packages("devtools")
devtools::install_github("vitormarquesr/qwalkr")
```
## Usage
To use `qwalkr`, you must first load it like so:
```{r}
library(qwalkr)
```
Now, you are all set.
## Creating a Quantum Walk
At the moment, `qwalkr` only supports continuous-time quantum walks.
You can create a continuous-time quantum walk by calling `qwalkr::ctqwalk()` with an additional argument representing the Hamiltonian of the system i.e. a hermitian matrix. For instance, let's create a walk on the complete graph of order three ($K_3$), the Hamiltonian is going to be its adjacency matrix.
```{r}
K3 <- rbind(c(0, 1, 1),
c(1, 0, 1),
c(1, 1, 0))
w <- ctqwalk(hamiltonian = K3)
```
The `ctqwalk` object stores the spectral decomposition of the Hamiltonian for future usage by other methods. We can get a glimpse of the eigenvalues and their multiplicities by printing the object.
```{r}
w
```
`qwalkr::ctqwalk()` runs `qwalkr::spectral()` under the hood and tries to infer eigenvalue multiplicity, you can disable this feature with `multiplicity=FALSE` or set the tolerance for numerical equality with `tol`.
Let's talk a bit about `spectral`, the linear algebra system used by `qwalkr`.
## Linear Algebra System - Spectral
`qwalkr::spectral()` is a constructor for class `spectral` which enhances base R spectral decomposition function `base::eigen()`, providing specialized treatment for hermitian matrices. Its constructor and methods can handle repeated eigenvalues, orthogonal projectors, matrix functions, and so on.
You can create an instance by providing a hermitian matrix to `qwalkr::spectral()`:
```{r}
A <- rbind(c(0, 1, 1),
c(1, 0, 1),
c(1, 1, 0))
s <- spectral(A)
```
The object stores the ordered eigenvalues (descending) and their multiplicities alongside their eigenvectors:
```{r}
s
```
You can turn off eigenvalue multiplicity inference with `multiplicity=FALSE` and set the tolerance for numerical equality with `tol=1e-10`.
### Methods
With a `spectral` instance in hand, we can extract several matrices of interest. Use `qwalkr::get_eigspace()` to extract the eigenspace associated with an eigenvalue. For instance, let's extract the eigenvectors corresponding to the eigenvalue -1 by providing its index in the ordered spectra (two in this case)
```{r}
get_eigspace(s, id = 2)
```
Since the eigenvalue -1 has multiplicity two, its eigenspace has dimension two as well, thus we get two eigenvectors. If we extract the 2-eigenspace, we only get one vector.
```{r}
get_eigspace(s, id=1)
```
We can extract the orthogonal projector onto the -1-eigenspace by calling `qwalkr::get_eigproj()` in the same way as the previous function:
```{r}
E2 <- get_eigproj(s, id=2)
E2
```
Since the eigenspace has dimension two, its trace (`qwalkr::tr()`) is going to take such a value
```{r}
tr(E2)
```
As to the 2-eigenspace we get
```{r}
E1 <- get_eigproj(s, id=1)
E1
tr(E1)
```
For additional methods for class spectral check its help page.
### Quantum Walks of class spectral
As with continuous-time quantum walks, some walks have their evolution influenced by the spectrum of the Hamiltonian. In such a case, the class representing the walk is also a child of `spectral` and thus inherits its methods. For instance, a `ctqwalk` object is also an instance of `spectral`, and hence the methods of the previous section apply:
```{r}
w
get_eigspace(w, id=2)
class(w)
```
## Time-Evolution
You can get the unitary time evolution operator of the walk by calling `qwalkr::unitary_matrix()` with the time `t` you want to evaluate it at. Let's take a look at the operator of the walk on $K_3$ at time $t=\frac{\pi}{3}$:
```{r}
unitary_matrix(w, t=pi/3)
```
Analogously, the mixing matrix of the walk can be obtained with `qwalkr::mixing_matrix()`:
```{r}
mixing_matrix(w, t=pi/3)
```
## Average Evolution
Use `qwalkr::avg_matrix()` to obtain the (standard) average mixing matrix:
```{r}
avg_matrix(w)
```
To instead calculate the (generalized) average mixing matrix under an arbitrary probability distribution, call `qwalkr::gavg_matrix()` with an additional parameter `R` containing samples of the desired distribution. For instance, the average mixing matrix of $K_3$ under a standard Exponential random variable can be obtained with:
```{r}
set.seed(10)
gavg_matrix(w, R=rexp(1000))
```