GraphDecoder
class GraphDecoder(code, S=None)
Class to construct the graph corresponding to the possible syndromes of a quantum error correction code, and then run suitable decoders.
Parameters
- code (RepitionCode) – The QEC Code object for which this decoder will be used.
- S (networkx.Graph) – Graph describing connectivity between syndrome elements. Will be generated automatically if not supplied.
Additional information:
The decoder for the supplied code is initialized by running _make_syndrome_graph(). Since this process can take some time, it is also possible to load in a premade S. However, if this was created for a differently defined code, it won’t work properly.
Methods
get_logical_prob
GraphDecoder.get_logical_prob(results, algorithm='matching')
Parameters
- results (dict) – A results dictionary, as produced by the
- code. (process_results method of the) –
- algorithm (str) – Choice of which decoder to use.
Returns
Dictionary of logical error probabilities for each of the encoded logical states whose results were given in the input.
Return type
dict
make_error_graph
GraphDecoder.make_error_graph(string, subgraphs=None)
Parameters
- string (str) – A string describing the output from the code.
- subgraphs (list) – Used when multiple, semi-independent graphs need
- created. (need to) –
Returns
The subgraph(s) of S which corresponds to the non-trivial syndrome elements in the given string.
Return type
E
matching
GraphDecoder.matching(string)
Parameters
string (str) – A string describing the output from the code.
Returns
A string with corrected logical values,
computed using minimum weight perfect matching.
Return type
str
Additional information:
This function can be run directly, or used indirectly to calculate a logical error probability with get_logical_prob
weight_syndrome_graph
GraphDecoder.weight_syndrome_graph(results)
Generate weighted syndrome graph from result counts.
Parameters
- results (dict) – A results dictionary, as produced by the
- code. (process_results method of the) –
Additional information:
Uses results to estimate the probability of the errors that create the pairs of nodes in S. The edge weights are then replaced with the corresponding -log(p/(1-p).