This registration describes a preregistered confirmatory study of heterogeneous pre-decoder conditioning in a rotated surface-code quantum error-correction workflow. Its purpose is to test whether, at a fixed total conditioning budget, heterogeneously distributed upstream conditioning produces lower logical error than budget-matched uniform conditioning, whether a single frozen conditioned representation remains effective across an architecture-diverse downstream decoder set, and whether performance varies nontrivially across a preregistered ordered target:non-target ratio grid. The study is conducted under frozen distances, frozen operating points, frozen seed and shot schedules, preregistered pooled metrics, and locked success criteria so that any supported effect can be interpreted as evidence about the architecture under test rather than post hoc tuning. The design contains four preregistered arms: untreated control, budget-matched uniform conditioning, budget-matched differential conditioning, and an ordered ratio-sweep arm with fixed total burden. A central feature of the protocol is that the conditioned representation is formed once upstream and then passed unchanged to each confirmatory decoder family, with no decoder-specific retuning, remapping, or auxiliary adaptation permitted after formation. The minimum confirmatory decoder set is minimum-weight perfect matching, Union-Find, and BP+OSD, selected to evaluate transfer across structurally different decoder families. Expected outcomes are defined in confirmatory terms. The primary pooled causal endpoint tests whether budget-matched differential conditioning outperforms budget-matched uniform conditioning, while the primary pooled transfer endpoint tests whether the same frozen conditioned representation improves performance relative to untreated control across decoder families. Additional preregistered questions address tunability through the ordered ratio response and persistence of favorable pooled effects across more than one primary code distance. A strong confirmatory outcome requires favorable pooled results with two-sided 95% bootstrap confidence intervals excluding 1 in the favorable direction, plus distance persistence at two or more primary distances; mixed, null, and thesis-changing outcomes are also explicitly predefined. This registration is limited to architecture-level evaluation and reproducible public inference. It does not claim that the frozen partition rule is uniquely optimal, that any specific targeting heuristic is the sole source of benefit, that threshold or asymptotic scaling is established, or that the exact coupling law, score function, or targeting recipe is disclosed. The confirmatory claim is bounded to whether budget-matched heterogeneous upstream conditioning improves logical performance and transfers across decoder families when the upstream representation is held fixed.
This registration describes a preregistered confirmatory study of heterogeneous pre-decoder conditioning in a rotated surface-code quantum error-correction workflow. Its purpose is to test whether, at a fixed total conditioning budget, heterogeneously distributed upstream conditioning produces lower logical error than budget-matched uniform conditioning, whether a single frozen conditioned representation remains effective across an architecture-diverse downstream decoder set, and whether performance varies nontrivially across a preregistered ordered target:non-target ratio grid. The study is conducted under frozen distances, frozen operating points, frozen seed and shot schedules, preregistered pooled metrics, and locked success criteria so that any supported effect can be interpreted as evidence about the architecture under test rather than post hoc tuning. The design contains four preregistered arms: untreated control, budget-matched uniform conditioning, budget-matched differential conditioning, and an ordered ratio-sweep arm with fixed total burden. A central feature of the protocol is that the conditioned representation is formed once upstream and then passed unchanged to each confirmatory decoder family, with no decoder-specific retuning, remapping, or auxiliary adaptation permitted after formation. The minimum confirmatory decoder set is minimum-weight perfect matching, Union-Find, and BP+OSD, selected to evaluate transfer across structurally different decoder families. Expected outcomes are defined in confirmatory terms. The primary pooled causal endpoint tests whether budget-matched differential conditioning outperforms budget-matched uniform conditioning, while the primary pooled transfer endpoint tests whether the same frozen conditioned representation improves performance relative to untreated control across decoder families. Additional preregistered questions address tunability through the ordered ratio response and persistence of favorable pooled effects across more than one primary code distance. A strong confirmatory outcome requires favorable pooled results with two-sided 95% bootstrap confidence intervals excluding 1 in the favorable direction, plus distance persistence at two or more primary distances; mixed, null, and thesis-changing outcomes are also explicitly predefined. This registration is limited to architecture-level evaluation and reproducible public inference. It does not claim that the frozen partition rule is uniquely optimal, that any specific targeting heuristic is the sole source of benefit, that threshold or asymptotic scaling is established, or that the exact coupling law, score function, or targeting recipe is disclosed. The confirmatory claim is bounded to whether budget-matched heterogeneous upstream conditioning improves logical performance and transfers across decoder families when the upstream representation is held fixed.
Component B contains the architecture-level evaluation materials for a preregistered study of heterogeneous pre-decoder conditioning in surface-code quantum error correction. It is focused on whether, at fixed total conditioning budget, heterogeneously applied upstream conditioning produces a reproducible logical-performance benefit relative to budget-matched uniform conditioning and whether a single frozen conditioned representation transfers across multiple downstream decoder families without decoder-specific retuning. Public-facing interpretation is limited to confirmatory architecture-level behavior and does not disclose implementation-specific reconstruction details, exact targeting logic, or exact conditioning rules.
This preregistration describes a confirmatory study of an upstream conditioning architecture in a quantum error-correction workflow. The project’s purpose is to test whether an intervention applied prior to the primary decoder produces a real and reproducible decoder-relevant effect, remains compatible with more than one downstream decoder family, generates a measurable exposed-observable statistical footprint, and improves or preserves performance across a preregistered regime rather than only at a single operating point. The study is intentionally limited to architecture-level evaluation, with the public claim set confined to exposed observables, frozen transformations, preregistered statistical tests, explicit negative controls, and bounded interpretation rules. The confirmatory design is organized around five preregistered tests: category existence, decoder compatibility, structural footprint, observable statistical signature, and regime robustness. Across these tests, the central comparison is between a baseline control pipeline and a conditioned pipeline in which the upstream intervention is applied while all non-conditioning settings are held fixed. The study uses frozen observables and frozen execution rules, including matched seeds, shot counts, code distances, physical error rates, geometry, and baseline decoder settings, so that any supported effect can be interpreted as evidence about the architecture under test rather than as a byproduct of post hoc tuning. To preserve a clean-room boundary, the registration restricts primary analysis to exposed, decoder-facing quantities only. These include logical failure, an exposed defect-count summary, and an exposed decoder-difficulty proxy, along with a locked scalar error-load construction derived from control-only standardization. The study does not rely on decoder-internal state variables, hidden confidence traces, or implementation-specific internal diagnostics in its primary confirmatory analyses. This structure is intended to let readers evaluate whether the architecture leaves a reproducible footprint in decoder-facing behavior without disclosing reconstruction-level mechanism details. Expected outcomes are defined in confirmatory rather than speculative terms. If the preregistered hypotheses are supported, the study is expected to provide evidence that an upstream conditioning intervention exists as a real architectural category in QEC, acts before the primary decoder, is compatible with downstream decoding, changes decoder-relevant exposed structure, and produces a reproducible observable statistical footprint in logical-failure behavior. If support is absent, mixed, or null, those outcomes will still be recorded under the frozen rules without rescue through post hoc metric changes, reweighting, or reinterpretation. In either case, the expected result of the registration is a bounded, auditable public record of whether this architecture class shows reproducible category-level behavior under a preregistered confirmatory design. The preregistration is not intended to establish a unique mechanism identity, identify a privileged implementation, disclose implementation-specific reconstruction details, or support claims about exact targeting logic, suppression laws, subset-sizing logic, telemetry-governed control, or broader cross-domain theory narratives. Public-facing interpretation is therefore intentionally limited to category validation and exposed-observable behavior. This is meant to support rigorous public evaluation while preserving strict clean-room limits around protected implementation detail.
This project presents a preregistered, confirmatory evaluation of a state-dependent pre-decoder conditioning approach in quantum error correction (QEC). The study investigates whether an upstream intervention applied to syndrome-derived information—prior to decoding—produces a consistent and reproducible effect on downstream decoding behavior under controlled conditions. The central objective is to evaluate the existence, robustness, and generality of an upstream conditioning effect at the architectural level, without assuming or asserting a specific internal mechanism. The intervention is treated as a black-box transformation that modifies the representation of syndrome information before it is provided to a decoder, enabling assessment of its observable footprint in decoder-relevant outcomes. The experimental design follows a fully specified, preregistered protocol with all primary elements defined in advance. A four-arm structure is employed: (i) an untreated control condition, (ii) a uniformly applied intervention, (iii) a state-dependent differential intervention, and (iv) a budget-matched comparison that isolates allocation effects under fixed total intervention magnitude. All arms are evaluated across fixed code distances and physical error-rate grids using shared seed schedules and identical decoding backends. Evaluation is conducted in a decoder-agnostic framework in which conditioned representations are generated once per instance and routed unchanged to multiple downstream decoders. This ensures that any observed differences arise from the upstream intervention rather than decoder-specific adaptation or tuning. Primary outcomes are defined in terms of logical failure behavior and exposed error-load observables derived from each instance. These observables are combined into a standardized composite metric using control-only normalization procedures, with all transformations, binning rules, and statistical tests specified in advance and frozen prior to confirmatory analysis. Conditional analyses are performed using bins defined exclusively from control distributions, with consistent application across all experimental arms. The preregistration includes explicit governance rules covering pilot-to-confirmatory transition, parameter freeze conditions, and the handling of any deviations. Pilot-authorized adjustments (such as parameter grid refinement or binning resolution) are finalized and documented prior to continuation of confirmatory execution. No post hoc modification of primary endpoints, evaluation criteria, or aggregation procedures is permitted. The expected outcome of this study is not a predetermined direction of effect, but a rigorous determination of whether an upstream conditioning intervention produces a measurable, reproducible, and structurally consistent footprint in decoder-relevant behavior under preregistered conditions. Results will be reported strictly according to the predefined analysis plan, with negative controls and reproducibility checks included to validate robustness. This work is intended to establish or refute the presence of an architectural-level effect in QEC pipelines while maintaining a strict separation between publicly disclosed evaluation structure and non-public implementation details.
Fast, scalable decoding architectures that operate in a block-wise parallel fashion across space and time are essential for real-time fault-tolerant quantum computing. We introduce a scalable AI-based pre-decoder for the surface code that performs local, parallel error correction with low decoding runtimes, removing the majority of physical errors before passing residual syndromes to a downstream global decoder. This modular architecture is backend-agnostic and composes with arbitrary global decoding algorithms designed for surface codes, and our implementation is completely open source. Integrated with uncorrelated PyMatching, the pipeline achieves end-to-end decoding runtimes of order O(1μs) per round at large code distances on NVIDIA GB300 GPUs while reducing logical error rates (LERs) relative to global decoding alone. In a block-wise parallel decoding scheme with access to multiple GPUs, the decoding runtime can be reduced to well below O(1μs) per round. We observe further LER improvements by training a larger model, outperforming correlated PyMatching up to distance-13. We additionally introduce a noise-learning architecture that infers decoding weights directly from experimentally accessible syndrome statistics without requiring an explicit circuit-level noise model. We show that purely data-driven graph weight estimation can nearly match uncorrelated PyMatching and exceed correlated PyMatching in certain regimes, enabling highly-optimized decoding when hardware noise models are unknown or time-varying, as well as training pre-decoders with realistic noise models. Together, these results establish a practical, modular, and high-throughput decoding framework suitable for large-distance surface-code implementations.
This preregistration defines a confirmatory evaluation of whether an upstream conditioning architecture in surface-code quantum error correction exhibits a favorable system-level burden tradeoff under fixed-budget constraints. The study introduces no new simulations, no new observables, and no decoder-internal instrumentation. Instead, it analyzes only preregistered artifacts from the frozen Megasweep v4 design, using architecture-level, decoder-facing quantities to compare reductions in downstream burden against corresponding upstream conditioning burden. The purpose of this component is narrower than the earlier architecture and heterogeneity components. Components A and B evaluated whether an abstract upstream intervention produces a real, reproducible structural footprint and whether differential conditioning outperforms uniform allocation under matched-budget conditions. Component C extends that framework by evaluating whether the observed downstream burden relief remains favorable when considered as a system-level tradeoff within the preregistered operating regime. All metrics, normalization rules, operating-regime definitions, and closure criteria are specified in advance under the same Clean Room governance used throughout the project. The analysis is restricted to frozen datasets, frozen design conditions, and exposed observables only. No decoder-internal telemetry, hardware-specific latency claims, FPGA implementation claims, or mechanism-level interpretations are used. This component is intended to support statements about system-level burden tradeoffs at the architecture level while preserving the project’s interpretation ceiling and implementation-agnostic disclosure posture. Expected outputs include a preregistered figure showing the net-efficiency metric across ratio levels and distances, together with predefined sensitivity views under alternative upstream normalizations. Confirmatory outcomes are defined in advance: whether net efficiency remains positive in the frozen primary operating regimes, whether downstream burden relief is accompanied by non-adverse directional change in the exposed structural cross-check, and whether the sign and within-distance ordering of the burden-tradeoff result remain stable under predefined sensitivity analyses. More broadly, this component is designed to provide a governed, reproducible evaluation of system-level burden tradeoff without exceeding the scope of the preregistered architecture-level evidence.
This project presents a preregistered, confirmatory study of an upstream conditioning architecture in quantum error correction (QEC). It evaluates whether an intervention applied to syndrome-derived information prior to decoding produces a measurable, reproducible effect on downstream decoding behavior under frozen conditions. The study is designed to test the existence, robustness, and decoder compatibility of an upstream conditioning effect at the architectural level without asserting a specific internal mechanism. The intervention is treated as a black-box transformation that produces a conditioned representation for downstream decoding, enabling evaluation of its observable footprint in decoder-relevant outcomes. The experimental program follows a fully specified preregistered protocol and evaluates control and conditioned regimes across fixed code distances and physical error-rate settings using a shared confirmatory design. A decoder-agnostic framework is used in which conditioned representations are generated upstream and then routed unchanged to multiple downstream decoder families. Primary outcomes are defined in terms of logical failure behavior and exposed decoder-facing observables under preregistered analysis rules, with negative controls and reproducibility checks included. The aim is to determine, under preregistered conditions, whether an upstream conditioning intervention produces a reproducible architectural-level effect in QEC pipelines while maintaining a strict separation between publicly disclosed evaluation structure and non-public implementation details.