Managing Classical Processing Requirements for Quantum Error Correction

Quantum Error Correction requires decoders to process syndromes generated by the error-correction circuits. These decoders must process syndromes faster than they are being generated to prevent a backlog of undecoded syndromes. This backlog can exponentially increase the time required to execute the program, which has resulted in the development of fast hardware decoders that accelerate decoding. Applications utilizing error-corrected quantum computers will require hundreds to thousands of logical qubits and provisioning a hardware decoder for every logical qubit can be very costly. In this work, we present a framework to reduce the number of hardware decoders and navigate the compute-performace trade-offs without sacrificing the performance or reliability of program execution. Through workload-centric characterizations performed by our framework, we propose efficient decoder scheduling policies that can reduce the number of hardware decoders required to run a program by up to 10X. With the proposed framework, scalability can be achieved via decoder virtualization, and individual decoders can be built to maximize accuracy and performance.