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.
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.
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.
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.
Quantum error correction (QEC) enables fault-tolerant quantum computation but requires operating quantum hardware at physical error rates below code-dependent thresholds, which remains challenging for current devices. We introduce syndrome resampling, a general method that increases QEC thresholds of any decoder and suppresses logical errors without additional hardware, decoding modifications, or code-specific assumptions beyond syndrome statistics. The method exploits the fact that syndromes with low probability are likely to lead to logical failure, therefore biasing syndrome averages towards most likely syndromes effectively increases logical fidelities. We establish a direct connection between the Rényi coherent information (RCI) and powers of the syndrome probability distribution, showing that resampling syndromes according to these powers combined with maximum likelihood decoding (MLD) realizes a family of optimal thresholds associated with phase transitions in the RCI. Numerical simulations of surface codes demonstrate that syndrome resampling substantially increases thresholds for both optimal and suboptimal decoders and reduces logical error rates by up to four orders of magnitude in experimentally relevant regimes. We further show that syndrome resampling can be effectively implemented from finite data and combined with decoding-based post-selection to achieve additional gains. Finally, applying the method to existing experimental QEC data yields up to two orders of magnitude reduction in logical error rates without requiring additional measurements. Our results provide a practical and decoder-agnostic route to improved logical fidelities in near-term QEC experiments.
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.
Quantum error correction (QEC) is essential for realizing fault-tolerant quantum computation. Current QEC controllers execute all scheduled syndrome (parity-bit) measurement rounds before decoding, even when early syndrome data indicates that the run will result in an error. The resulting excess measurements increase the decoder's workload and system latency. To address this, we introduce an adaptive abort module that simultaneously reduces decoder overhead and suppresses logical error rates in surface codes and color codes under an existing QEC controller. The key idea is that initial syndrome information allows the controller to terminate risky shots early before additional resources are spent. An effective scheme balances the cost of further measurement against the restart cost and thus increases decoder efficiency.
Adaptive abort schemes dynamically adjust the number of syndrome measurement rounds per shot using real-time syndrome information. We consider three schemes: fixed-depth (FD) decoding (the standard non-adaptive approach used in current state-of-the-art QEC controllers), and two adaptive schemes, AdAbort and One-Step Lookahead (OSLA) decoding. For surface and color codes under a realistic circuit-level depolarizing noise model, AdAbort substantially outperforms both OSLA and FD, yielding higher decoder efficiency across a broad range of code distances. Numerically, as the code distance increases from 5 to 15, AdAbort yields an improvement that increases from 5% to 35% for surface codes and from 7% to 60% for color codes.
To our knowledge, these are the first adaptive abort schemes considered for QEC. Our results highlight the potential importance of abort rules for increasing efficiency as we scale to large, resource-intensive quantum architectures.
Component A contains the architecture-level evaluation materials for a preregistered study of an upstream conditioning intervention in quantum error correction. The study tests whether an intervention applied before the primary decoder produces a reproducible decoder-relevant statistical footprint and improves or preserves decoder-relevant performance under frozen observables, preregistered tests, explicit negative controls, and bounded interpretation rules. It is intended to validate the existence, decoder compatibility, and exposed-observable footprint of the architecture without disclosing implementation-specific reconstruction details or claiming unique mechanism identity.