abv/templates/doc.md

ABP server

This server does a few things:

• Keeps a graph state in memory using a data structure that is compatible with abp
• Serves a JSON representation of that object
• Accepts updates to that object via POST requests
• Displays a 3D representation of the state
• Randomly updates the state every five seconds

Using the interface

Sessions are identified by a UUID such as oranges-arkansas-mexico-fish. You can share this URL with other people to share your screen and edit collaboratively.

• Click on the grid to make a new node
• Ctrl-click a node to act a Hadamard gate
• Select a node, then shift-click another node to act a CZ gate
• Press space to rotate the grid

Python package

The underlying graph state simulator is based on Anders’ and Briegel's method. Full docs for the Python package are here.

API

We expose an API so that you can programmatically read and write states to/from the server and simulate mouse clicks.

Endpoints

• /<uuid>: Displays the state using Three.js
• /<uuid>/graph:
  • GET returns JSON representing the state
  • POST accepts JSON in the same format and overwrites the state in memory
• /<uuid>/edit:
  • POST accepts edit commands such as cz, add_node etc.
• doc/: Shows this page

Data format

If you do an HTTPS GET against /<uuid>/graph, you will receive some JSON.

:::bash
$ curl https://abv.peteshadbolt.co.uk/<uuid>/graph

outputs

:::python
{"node": 
    {"30": 
        {"position": 
            {"y": 3.245091885135617, "x": -1.0335390368621762, "z": 0.12485495696298532}, "vop": 0}, "28": 
        {"position": 
            {"y": 0.1811335599620998, "x": 3.7102305790943295, "z": 0.3375519427305571}, "vop": 0}, "29": 
        {"position": 
            {"y": -1.834182888403804, "x": 1.5968911365745622, "z": 2.8585980299131886 
...

The top-level keys are node and adj. These model the node metadata and adjacency matrix respectively.

Each node has

• a position ({x:<> y:<> z:<>})
• a vop (integer, ignore for now)
• and could also have a color, label, etc.

adj uses the same data structure as networkx to efficiently represent sparse adjacency matrices. For each key i in adj, the value of adj[i] is itself a map whose keys j correspond to the ids of nodes connected to i. The value of adj[i][j] is a map which is usually empty but which could be used to store metadata about the edge.

Here's an example of a graph (A-B C):

:::python
{'adj': {0: {1: {}}, 1: {0: {}}, 2: {}},
 'node': {
  0: {'position': {'x': 0, 'y': 0, 'z': 0}, 'vop': 0},
  1: {'position': {'x': 1, 'y': 0, 'z': 0}, 'vop': 0},
  2: {'position': {'x': 2, 'y': 0, 'z': 0}, 'vop': 10}}}