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 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 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 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.
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.
Quantum error correction (QEC) enables reliable computation on noisy hardware by encoding logical information across many physical qubits and periodically measuring parities to detect errors. A decoder is the classical algorithm that uses these measurements to infer which error most likely occurred, so that the system can correct it. The decoder's accuracy-how rarely it makes the wrong guess-directly determines the scale of quantum computation that can be reliably executed. With a wealth of competing decoding algorithms, a QEC system designer needs reliable methods to evaluate them. Today, the dominant approach is to evaluate decoders using Monte Carlo simulation. However, simulation has several drawbacks such as requiring many samples to produce low variance estimates.
In this work, we develop a new systematic analysis for evaluating decoders. We introduce a novel formal semantics of a core language for QEC programs that captures the de facto standard Stim circuit format, providing a principled theoretical foundation for the emerging space of fault-tolerant quantum systems design. Given a QEC program and a decoder, our verifier can quantify both the decoder accuracy and the decoder robustness to drift in physical error rate. Our approach has two key components: (i) a structured search over the space of possible errors; and (ii) a constrained polynomial optimization kernel. A thorough empirical evaluation of our approach suggests that it can outperform simulation, especially in low error rate regimes, and that it can be deployed to quantify decoder robustness over an interval of physical error rates.
Quantum error correction (QEC) is the most promising route toward fault-tolerant quantum computing and, thus, useful quantum computers. QEC operates as a continuous measure-decode-correct cycle: ancilla qubits are read out, a decoder infers errors from the resulting syndromes, and corrections are applied before the next round begins. Within this loop, readout occupies a uniquely critical role, as it is the sole source of ground truth available to the decoder. Yet readout is also the slowest and most error-prone operation in the stack, with characteristics that vary across qubits and drift over time; This complexity propagates directly to the classical control hardware, and in particular to the FPGA-hosted machine-learning (ML) discriminator that must classify each analog signal into a binary syndrome outcome. Despite this central role, QEC performance has not yet been studied in depth from the perspective of readout characteristics, readout length, and their co-design with an ML discriminator.
We introduce Oraqle, an end-to-end benchmarking framework that evaluates qubit-state readout and its impact on QEC performance across real experimentally extracted qubit-state-readout datasets, state-of-the-art ML discriminators, multiple QEC codes, and hardware regimes spanning current to projected devices. Our study reveals three asymmetric findings: The measurement duration can be significantly reduced with nearly no penalty to the logical error rate; The discriminator complexity barely affects the QEC performance, as residual errors are written into device physics rather than the model; and the impact of qubit-state readout on the logical error rate is conditional on where the hardware sits in the QEC landscape, a window that widens as devices mature.
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.