<commit_before># An exact diagonalization implementation for quantum-dot cellular automata

## Overview

An exact diagonalization implementation for computer simulations of quantum-dot
cellular automata (QCA), a beyond-CMOS computing paradigm ([Original paper proposing
QCA][Lent1993]).

The simulation is written in C++, but exposed as a Python extension module, and
uses [Eigen][] as a linear algebra library and [Boost][] for the Python
integration and unit testing.

## Installation

### Prebuilt binaries

Prebuilt OS X and Linux binary packages for use with the [Anaconda][] Python distribution (or
another distribution using the [conda][] package manager) are available on
[Binstar][]. To use them, first install Anaconda, and then install the qca package
from Binstar.
```
$ conda install -c https://conda.binstar.org/meznom qca
```
This will pull in all dependencies and install everything into the main Anaconda
environment. One notable dependency is boost-python which is also provided by my
Binstar channel.

Alternatively, you can create a new environment and install the qca package
there.
```
# Add my Binstar channel permanently to your conda config (~/.condarc)
$ conda config --add channels http://conda.binstar.org/meznom
# Create a new environment 'qca' with a number of useful packages
$ conda create -n qca qca coma ipython ipython-notebook matplotlib numpy scipy
# Activate the 'qca' environment
$ source activate qca
# Do some work, as an example, start the IPython notebook
$ ipython notebook
# Deactivate the 'qca' environment<commit_msg>README.md edited online with Bitbucket<commit_after># An exact diagonalization implementation for quantum-dot cellular automata

## Overview

An exact diagonalization implementation for computer simulations of quantum-dot
cellular automata (QCA), a beyond-CMOS computing paradigm ([Original paper proposing
QCA][Lent1993]).

The simulation is written in C++, but exposed as a Python extension module, and
uses [Eigen][] as a linear algebra library and [Boost][] for the Python
integration and unit testing.

## Installation

### Prebuilt binaries

Prebuilt OS X and Linux binary packages for use with the [Anaconda][] Python distribution (or
another distribution using the [conda][] package manager) are available on
[Binstar][]. To use them, first install Anaconda, and then install the qca package
from Binstar.
```
$ conda install -c https://conda.binstar.org/meznom qca
```
This will pull in all dependencies and install everything into the main Anaconda
environment. One notable dependency is boost-python which is also provided by my
Binstar channel.

Alternatively, you can create a new environment and install the qca package
there.
```bash
# Add my Binstar channel permanently to your conda config (~/.condarc)
$ conda config --add channels http://conda.binstar.org/meznom
# Create a new environment 'qca' with a number of useful packages
$ conda create -n qca qca coma ipython ipython-notebook matplotlib numpy scipy
# Activate the 'qca' environment
$ source activate qca
# Do some work, as an example, start the IPython notebook
$ ipython notebook
# Deactivate the 'qca' environment
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