Local pre-matching pass for correlated decoding of quantum error correcting codes

1 . A computer implemented method comprising:

obtaining measurement data from a quantum computer that performs a quantum computation;

generating, using the measurement data, a first detector graph, wherein the first detector graph labels a first set of detection events that occur in the measurement data and each edge in the first detector graph is weighted;

generating, using the measurement data, a second detector graph, wherein the second detector graph labels a second set of detection events that occur in the measurement data, the second set of detection events being different to the first set of detection events, and each edge in the second detector graph is weighted;

for each detection event in the first detector graph:

identifying one or more edges in the first detector graph that are incident to the detection event; and

processing the identified one or more edges in sequence, comprising, for each edge that is incident to the detection event and is connected to another detection event, labelling the edge as a candidate error mechanism; and

for each edge labelled as a candidate error mechanism, updating respective weights of one or more complementary edges in the second detector graph to generate an updated second detector graph; and

executing a decoding process on the updated second detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation.

2 . The method of claim 1 , wherein the first detector graph and the second detector graph are generated from a hypergraph that represents the measurement data.

3 . The method of claim 2 , wherein one or more edges in the second detector graph are complementary edges of an edge in the first detector graph when the edge in the first detector graph and the edges in the second detector graph combine into a single hyperedge of the hypergraph.

4 . The method of claim 1 , wherein generating the first detector graph and generating the second detector graph comprises:

generating a hypergraph that represents the measurement data; and

decomposing the hypergraph into the first detector graph and the second detector graph, wherein the first detector graph and the second detector graph are disjoint graphs.

5 . The method of claim 1 , wherein updating respective weights of one or more complementary edges in the second detector graph comprises performing Bayesian reweighting of the complementary edges in the second detector graph.

6 . The method of claim 1 , wherein the first set of detection events comprise Pauli errors of a first type and the second set of detection events comprise Pauli errors of a second type, wherein the first type is different to the second type.

7 . The method of claim 6 , wherein the first type comprises X or Z errors and the second type comprises Z or X errors.

8 . The method of claim 6 , wherein the first detector graph and the second detector graph are generated from a hypergraph that represents the measurement data and the hypergraph represents Pauli errors of a third type that are decomposed as Pauli errors of the first type and Pauli errors of the second type.

9 . The method of claim 8 , wherein the Pauli errors of the third type comprise Y errors.

10 . The method of claim 1 , wherein generating the first detector graph and the second detector graph comprises associating each edge in the first detector graph and each edge in the second detector graph with an equal initial weight.

11 . The method of claim 1 , wherein processing the identified one or more edges in sequence comprises processing the identified one or more edges in ascending weight order.

12 . The method of claim 1 , wherein processing the identified one or more edges in sequence further comprises, for each edge that is incident to the detection event and is a detector graph boundary edge, processing a subsequent edge of the identified one or more edges.

13 . The method of claim 1 , wherein processing the identified one or more edges in sequence further comprises, for each edge that is incident to the detection event and is connected to another node that does not correspond to a detection event, processing a subsequent edge of the identified one or more edges.

14 . The method of claim 1 , wherein the decoding process comprises a minimum weight perfect matching decoding process or a union-find decoding process.

15 . The method of claim 1 , wherein labelling an edge as a candidate error mechanism indicates that an error associated to the error mechanism has occurred.

16 . A system comprising:

one or more data processing apparatuses; and

non-transitory computer readable storage media in data communication with the one or more data processing apparatuses and storing instructions that, when executed by the data processing apparatuses, cause the one or more data processing apparatuses to perform operations for decoding measurement data received from a quantum computer that performs a quantum computation, the operations comprising:

obtaining measurement data from a quantum computer that performs a quantum computation;

generating, using the measurement data, a first detector graph, wherein the first detector graph labels a first set of detection events that occur in the measurement data and each edge in the first detector graph is weighted;

generating, using the measurement data, a second detector graph, wherein the second detector graph labels a second set of detection events that occur in the measurement data, the second set of detection events being different to the first set of detection events, and each edge in the second detector graph is weighted;

for each detection event in the first detector graph:

identifying one or more edges in the first detector graph that are incident to the detection event; and

processing the identified one or more edges in sequence, comprising, for each edge that is incident to the detection event and is connected to another detection event, labelling the edge as a candidate error mechanism; and

for each edge labelled as a candidate error mechanism, updating respective weights of one or more complementary edges in the second detector graph to generate an updated second detector graph; and

executing a decoding process on the updated second detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation.

17 . A computer-readable storage medium comprising instructions stored thereon that are executable by a processing device and upon such execution cause the processing device to perform operations for decoding measurement data received from a quantum computer that performs a quantum computation, the operations comprising:

obtaining measurement data from a quantum computer that performs a quantum computation;

generating, using the measurement data, a first detector graph, wherein the first detector graph labels a first set of detection events that occur in the measurement data and each edge in the first detector graph is weighted;

generating, using the measurement data, a second detector graph, wherein the second detector graph labels a second set of detection events that occur in the measurement data, the second set of detection events being different to the first set of detection events, and each edge in the second detector graph is weighted;

for each detection event in the first detector graph:

identifying one or more edges in the first detector graph that are incident to the detection event; and

processing the identified one or more edges in sequence, comprising, for each edge that is incident to the detection event and is connected to another detection event, labelling the edge as a candidate error mechanism; and

for each edge labelled as a candidate error mechanism, updating respective weights of one or more complementary edges in the second detector graph to generate an updated second detector graph; and

executing a decoding process on the updated second detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation.