Syndrome Compression Pipeline for Quantum Error Decoder Bandwidth
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Solution Overview
Problem
Current quantum error correction decoders face challenges in scalability and resource efficiency due to high hardware costs and bandwidth requirements, especially when operating in cryogenic environments, as they struggle to process syndrome measurements efficiently across a large number of logical qubits.
Innovation Solution
A 3-stage pipelined micro-architecture for the Union-Find decoder is designed, incorporating compression engines to compress syndrome data, which reduces bandwidth overheads and enables high-throughput transit, and resource sharing across multiple logical qubits to minimize hardware costs without impacting error thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If syndrome data is transmitted without compression from quantum register to decoder, then all syndrome information is preserved for error correction, but bandwidth overhead increases and hardware costs rise
Solution Approach 1:
The patent extracts and removes redundant information from syndrome data before transmission. Compression engines identify and eliminate duplicate or unnecessary syndrome bits, transmitting only the essential error correction information from the quantum register to the decoder, thereby reducing bandwidth requirements while preserving error correction capability.
Solution Approach 2:
The patent changes the parameter of data representation by applying compression algorithms that transform full syndrome data into a compressed format. This parameter change reduces the volume of data transmitted over the interconnect, lowering bandwidth overhead and hardware costs while maintaining the ability to perform error correction.
2Device complexity
If syndrome data is compressed before transmission, then bandwidth overhead is reduced and hardware costs decrease, but data transmission speed may be affected
Solution Approach 1:
The patent applies compression engines at the quantum register level before data transmission occurs. By performing compression in advance (preliminary action), the system reduces the volume of data that needs to be transmitted over the interconnect, thereby reducing bandwidth overhead and hardware costs without impacting the speed of error correction processing.
3Device complexity
If resource sharing is implemented across multiple logical qubits, then hardware costs are reduced, but processing capacity may be constrained
Solution Approach 1:
The patent implements resource sharing where decoders and compression engines serve multiple logical qubits simultaneously. This multi-functional approach allows the same hardware resources to be universally applied across different quantum registers, reducing overall hardware costs while maintaining the processing capacity to handle syndrome data from multiple qubits through efficient resource allocation.
Data Source
AI summary
A quantum computing device comprises at least one quantum register including a plurality of logical qubits. A compression engine is coupled to each logical qubit of the plurality of logical qubits. Each compression engine is configured to compress syndrome data. A decompression engine is coupled to each compression engine. Each decompression engine is configured to receive compressed syndrome data, decompress the received compressed syndrome data, and route the decompressed syndrome data to a decoder block.