Alexander Knapen

Alexander Knapen, Junyi Luo, Guanchen Tao et al.

Quantum-classical interfaces (QCIs) for fault-tolerant quantum computing must manage simultaneous, real-time decoding across thousands to millions of logical qubits. Scaling these architectures necessitates sharing expensive decoding resources among logical qubits, which introduces severe resource contention within the QCI. While resolving these bottlenecks through efficient resource distribution remains a persistent challenge, lightweight predecoding holds promise to alleviate strain on shared decoding components by decreasing average latency and decoder usage. Notably, research into both decoder allocation and predecoding has been strictly confined to the surface code. With the growing emphasis on general quantum low-density parity-check (qLDPC) codes, slower decoding speeds will intensify resource contention, while the inherent complexity of these codes will render manual predecoder design unfeasible. To address this gap, we introduce an automated framework designed to generate predecoders for arbitrary qLDPC codes. These automatically constructed predecoders autonomously process over 90% of the decoding workload, cutting overall decoder utilization by up to 3,963x. This includes a reduction of up to 72.71% in computationally demanding ordered statistics decoding (OSD). Furthermore, we detail a highly efficient, pipelined hardware design that allows for the concurrent decoding of approximately 1,200 bivariate bicycle (BB) code logical qubits using a single FPGA. When implemented as a cryogenic ASIC, the architecture scales to support between 36,000 and 360,000 BB code logical qubits, operating within a 1.5 W power limit at 4 K.

Guanchen Tao, Alexander Knapen, Jacob Mack et al.

Scaling fault tolerant quantum computing is increasingly constrained by the limited bandwidth and power budget across the 4 K to room temperature (RT) interface. We present CryoZip, a cross stack cryogenic compression framework that cooperates with a lightweight cryogenic quantum error correction (QEC) predecoder to reduce 4 K to RT syndrome transmission under realistic, circuit level noise. CryoZip targets sparse syndrome vectors with a sliding window compression architecture sized under strict decoding latency constraints to maximize energy efficiency. We implement and evaluate the design in 22 nm FDSOI characterized at 4 K, using vector based power, performance, and area analysis to obtain realistic hardware data. CryoZip achieves up to 48x compression, 1.8x higher than state of the art compressors, across various QEC codes while delivering 4 to 26x energy savings. When paired with a QEC predecoder, it yields over 14,238x bandwidth reduction, while energy savings rise to 42x when accounting for realistic QEC interface overheads.