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This paper introduces PACE, a decoder-aware scheduling framework designed to optimize the use of transversal CNOT (TCNOT) gates in fault-tolerant quantum computation (FTQC) while managing the classical decoding workload. By employing techniques such as Hybrid Window Decoding, DEM Stitch, and Sub-window Parallel Decoding, PACE effectively balances the benefits of aggressive TCNOT scheduling with the constraints of classical decoding resources. The evaluation reveals a crucial trade-off between quantum acceleration and classical decoding costs, highlighting the limitations of existing decoding systems in the context of TCNOT-based FTQC.
Aggressive TCNOT scheduling can lead to significant quantum speedups, but it also risks overwhelming classical decoders, revealing a critical trade-off in FTQC performance.
Transversal CNOT (TCNOT) gates can accelerate fault-tolerant quantum computation (FTQC) in the surface code by reducing the number of syndrome extraction rounds required between logical operations from $O(d)$ to $O(1)$. This is particularly attractive for quantum platforms with long-range connectivity, such as neutral atoms. However, dense TCNOT schedules substantially increase the classical decoding workload. TCNOTs propagate errors across multiple surface-code patches, enlarging the spatiotemporal region that must be decoded jointly. Consequently, denser TCNOT schedules increase decoding latency and memory requirements and potentially exceed available decoder capacity. Moreover, because the detector error model (DEM) of each decoding window depends on the TCNOT schedule, exhaustively precomputing all possible window-level DEMs is infeasible, requiring just-in-time (JIT) DEM compilation. Thus, the practical benefit of TCNOT gates is limited not only by quantum hardware performance but also by classical decoding and DEM-compilation capacity. We introduce PACE, a decoder-aware scheduling framework for TCNOT-based FTQC. PACE first mitigates the decoder-side costs of aggressive TCNOT scheduling through three complementary techniques. Hybrid Window Decoding assigns different decoders for each decoding window according to its DEM structure. DEM Stitch generates schedule-specific window-level DEMs just in time by assembling reusable precompiled fragments. Sub-window Parallel Decoding decomposes large windows into smaller sub-windows with graph-coloring formulation. Building on these techniques, PACE then performs decoder-aware scheduling to maximize TCNOT concurrency within the available decoder resources. Our evaluation shows the trade-off between quantum acceleration and classical decoding cost, revealing the limitations of current decoding systems for TCNOT-based FTQC.
