Pete Shadbolt 6b68eb13bd | 6 years ago | |
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README.md | 6 years ago | |
app.py | 7 years ago | |
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test.py | 7 years ago |
Graph states are a way of efficiently representing the state of a system of qubits. This tool simulates the behaviour of the graph state and shows an interactive 3D representation of the state. Interaction with the simulation is done either by clicking on things in the web browser or through an API.
The state is initialized as a blank canvas without any qubits.
Arbitrary 3D structures can be constructed by rotating the grid.
The URL contains a unique ID such as 1ed6cc3c-65e4-4f6a-ab14-5e8c01d4593b
. You can share this URL with other people to share your screen and edit collaboratively.
The underlying graph state simulator is based on Anders’ and Briegel's method. Full docs for the Python package are here. You can install it like this:
:::bash
$ pip install abp
Here's a complete example of sending a state from Python to the server:
:::python
import abp
# Make a new graph and automatically position the nodes
g = abp.NXGraphState(range(10))
g.layout()
# Post to the server
g.push()
/<uuid>
: Displays the state using Three.js/<uuid>/graph
:
GET
returns JSON representing the statePOST
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 pageAn HTTP GET to /<uuid>/graph
will return some JSON.
$ curl https://abv.peteshadbolt.co.uk/<uuid>/graph
{
"adj": {
"(0, 0, 1)": {
"(0, 1, 1)": {},
"(1, 0, 1)": {}
},
"(0, 0, 3)": {
"(0, 1, 3)": {},
"(1, 0, 3)": {}
},
"(0, 0, 5)": {
"(0, 1, 5)": {},
"(1, 0, 5)": {}
},
"(0, 1, 0)": {
"(0, 1, 1)": {},
"(1, 1, 0)": {}
},
"(0, 1, 1)": {
"(0, 0, 1)": {},
...
The top-level keys are node
and adj
. These model the node metadata and adjacency matrix respectively.
Each node
has
position
({x:<> y:<> z:<>}
)vop
(integer, ignore for now)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}}}