Pipelined hardware decoder for quantum computing devices

Abstract

A quantum computing device comprises at least one quantum register including a plurality of qubits, and a hardware decoder. The hardware decoder is configured to: receive syndrome data from one or more of the plurality of qubits; and decode the received syndrome data by implementing a Union-Find decoding algorithm via a hardware microarchitecture including two or more pipeline stages.

1 . A quantum computing device, comprising:
 at least one quantum register including a plurality of qubits; and   a hardware decoder configured to:
 receive syndrome data from one or more of the plurality of qubits; and 
 decode the received syndrome data by implementing a Union-Find decoding algorithm via a hardware microarchitecture including two or more pipeline stages. 
   
     
     
         2 . The quantum computing device of  claim 1 , wherein the two or more pipeline stages include:
 a graph generator (Gr-Gen) module configured to:
 generate a spanning forest by growing clusters around non-trivial syndrome bits; and 
 store data regarding the spanning forest in a spanning tree memory (STM) and a zero data register; 
   a depth-first search (DFS) engine configured to:
 access data stored in the STM; and 
 generate one or more edge stacks based on the data stored in the STM; and 
   a correction (Corr) engine configured to:
 access the one or more edge stacks; 
 generate memory requests based on the accessed edge stacks; and 
 update an error log based on the decoded syndrome data. 
   
     
     
         3 . The quantum computing device of  claim 2 , wherein the DFS engine is further configured to generate:
 a primary edge stack including a list of visited edges,   a pending edge stack including a list of edges that will be visited; and   an alternate edge stack configured to hold surplus edges from a cluster of the spanning forest.   
     
     
         4 . The quantum computing device of  claim 1 , wherein at least one pipeline stage of the two or more pipeline stages is coupled to two or more upstream pipeline stages via a multiplexer. 
     
     
         5 . A quantum computing device, comprising:
 at least one quantum register including a plurality of logical qubits; and   a hardware decoder configured to receive syndrome data from one or more of the plurality of logical qubits, and to decode the received syndrome data, the hardware decoder comprising:
 a graph generator (Gr-Gen) module configured to:
 generate a spanning forest by growing clusters around non-trivial syndrome bits; and 
 store data regarding the spanning forest in a spanning tree memory (STM) and a zero data register; 
 
 a depth-first search (DFS) engine configured to:
 access data stored in the STM; and 
 generate one or more edge stacks based on the data stored in the STM; and 
 
 a correction (Corr) engine configured to:
 access one or more edge stacks; and 
 generate memory requests based on the accessed edge stacks. 
 
   
     
     
         6 . The quantum computing device of  claim 5 , wherein the Corr engine is further configured to:
 perform iterative peeling decoding on each accessed edge stack; and   update an error log of the hardware decoder based on results of the iterative peeling decoding.   
     
     
         7 . The quantum computing device of  claim 5 , wherein the hardware decoder analyzes d consecutive rounds of the received syndrome data together in a 3D graph, where dis a code distance. 
     
     
         8 . The quantum computing device of  claim 5 , wherein the Gr-Gen module is configured to generate the spanning forest using Union( )and Find( ) graph operations. 
     
     
         9 . The quantum computing device of  claim 5 , wherein the Gr-Gen module further includes a fusion edge stack configured to store newly grown edges. 
     
     
         10 . The quantum computing device of  claim 9 , wherein the STM is updated based on receiving an indication from the fusion edge stack that two or more clusters have merged. 
     
     
         11 . The quantum computing device of  claim 5 , wherein the DFS engine is configured to generate:
 a primary edge stack including a list of visited edges,   a pending edge stack including a list of edges that will be visited; and   an alternate edge stack configured to hold surplus edges from one or more clusters of the spanning forest.   
     
     
         12 . The quantum computing device of  claim 5 , wherein decompressed syndrome data is routed from a decompression engine to the Gr-Gen module. 
     
     
         13 . The quantum computing device of  claim 12 , wherein two or more decompression engines are coupled to the hardware decoder. 
     
     
         14 . A decoding method for a quantum computing device, comprising:
 receiving syndrome data from one or more of a plurality of qubits; and   decoding the syndrome data with a hardware-implemented Union-Find decoder including two or more pipeline stages.   
     
     
         15 . The decoding method of  claim 14 , wherein decoding the syndrome data with the hardware-implemented Union-Find decoder including the two or more pipeline stages comprises:
 at a graph generator (Gr-Gen) module:
 generating a spanning forest by growing clusters around non-trivial syndrome bits; and 
 storing data regarding the spanning forest in a spanning tree memory (STM) and a zero data register; 
   at a depth-first search (DFS) engine:
 accessing data stored in the STM; and 
 generating one or more edge stacks based on the data stored in the STM; and 
   at a correction (Corr) engine:
 accessing one or more of the generated edge stacks; and 
 generating memory requests based on the accessed edge stacks. 
   
     
     
         16 . The decoding method of  claim 15 , further comprising, at the Corr engine:
 performing iterative peeling decoding on each accessed edge stack; and   updating an error log of the hardware-implemented Union-Find decoder based on results of the iterative peeling decoding.   
     
     
         17 . The decoding method of  claim 15 , further comprising, analyzing d consecutive rounds of syndrome data together at the hardware-implemented Union-Find decoder in a 3D graph, where dis a code distance. 
     
     
         18 . The decoding method of  claim 15 , further comprising, at the Gr-Gen module, generating the spanning forest using Union( ) and Find( ) graph operations. 
     
     
         19 . The decoding method of  claim 15 , further comprising, at the Gr-Gen module, storing newly grown edges at a fusion edge stack. 
     
     
         20 . The decoding method of  claim 15 , further comprising, at the DFS engine:
 generating a primary edge stack including a list of visited edges;   generating a pending edge stack including a list of edges that will be visited; and   generating an alternate edge stack configured to hold surplus edges from a cluster of the spanning forest.