Empirical Falsification of Pairwise-Only Explanations for an Engineered Parity Benchmark on a 133-Qubit Superconducting Processor

• URL: http://arxiv.org/abs/2603.20542v1
• Date: Fri, 20 Mar 2026 22:24:41 GMT
• Title: Empirical Falsification of Pairwise-Only Explanations for an Engineered Parity Benchmark on a 133-Qubit Superconducting Processor
• Abstract summary: We report a compact hardware experiment designed to distinguish pairwise-only explanations from irreducible triplet-order predictive structure.<n>A1b protocol implements a parity-structured binary label on a 133-qubit IBM superconducting processor.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scalable quantum characterization and error-mitigation workflows often rely on the assumption that relevant device noise and readout contamination can be adequately captured by low-weight, predominantly pairwise interactions. We report a compact hardware experiment designed to operationally distinguish pairwise-only explanations from irreducible triplet-order predictive structure. The A1/A1b protocol implements a parity-structured binary label on a 133-qubit IBM superconducting processor (ibm_torino) and analyzes the resulting data through a classical M"obius decomposition of subset mutual informations. In the A1 baseline, we observe a macroscopic triplet correlation of f(123) = 0.72609 bits (p <= 1.0e-4, permutation floor). In the strict A1b loophole-reduction follow-up, role-symmetry averaging sharply suppresses singleton leakage, modestly reduces pairwise mismatch, and preserves a large irreducible triplet term of f(123) = 0.56521 bits. Crucially, a principled pairwise maximum-entropy baseline consistent with the empirical 1- and 2-body marginals implies only f(123) ~ 6.6e-6 bits, in strong contradiction with the observed hardware data. On A1b, a classifier built exclusively from pairwise features reaches only 0.617 held-out accuracy (chance 0.5), whereas a triplet-inclusive model reaches 0.910. These results provide a concise, open-data demonstration that pairwise benchmarking proxies can be fundamentally blind to higher-order contextual structure in present-day superconducting experiments.

Related papers

• Not All Relations Are Equal: Relation-Balanced and Calibrated Graph Learning for Provenance-Based Intrusion Detection [66.44527094471619]
  We present RECAL, an unsupervised framework using relation-balanced masked graph learning to better capture rare interaction patterns.<n>On three DARPA E3 datasets, RECAL achieves F1 scores of 99.99%, 99.93%, and 99.99%, outperforming the best baseline on each dataset by 0.88, 0.82, and 0.42 percentage points, respectively.
  arXiv  Detail & Related papers  (2026-09-15T00:31:01Z)
• DODR: Deterministic Operator-Driven Reasoning in Latent Space [0.0]
  This paper proposes the Deterministic Operator-Driven Reasoning in Latent Space architecture.<n>It reconstructs reasoning as reasoning-graph computation in a high-dimensional linear-algebraic space.<n>The architecture provides a structural zero-hallucination guarantee and a three-layer continual-learning mechanism.
  arXiv  Detail & Related papers  (2026-09-04T06:20:35Z)
• A Deterministic Binary Fingerprinting Framework with Zero-Trained Feature Extraction for Sparse Count Matrices [5.730395615441126]
  MMTB is a non-learned binary representation framework that requires no label supervision, model fitting, or gradient-based optimization.<n>Relative to dense float32 representations, MMTB fingerprints reduce memory by approximately 10-fold while providing fixed-width Hamming-indexable codes.
  arXiv  Detail & Related papers  (2026-07-16T05:47:52Z)
• Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems [47.027290803102666]
  Normal behaviour is the union of many imbalanced, curved, thin-fringed operating regimes.<n>We model the normal law with a jointly learned latent representation plus explicit Gaussian-mixture mode clustering.<n>We evaluate under a deliberately fair protocol: raw point-wise metrics with no point adjustment, a trivial-detector difficulty split, prevalence-matched F1, and train-normal-only calibration.
  arXiv  Detail & Related papers  (2026-07-07T10:10:00Z)
• PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception [75.70273182182397]
  PerceptionRubrics is a rubric-based evaluation framework.<n>It addresses the gap between saturated benchmark scores and real-world brittleness.<n>It pairs 1,038 information-dense images with over 10,000 instance-specific rubrics.
  arXiv  Detail & Related papers  (2026-06-26T17:59:15Z)
• Transformers Can Learn Rules They've Never Seen: Proof of Computation Beyond Interpolation [0.0]
  We test a strong-only hypothesis in two controlled settings.<n>In Experiment 1, we use a cellular automaton with a pure XOR transition rule.<n>In Experiment 2, we study symbolic operator chains over integers with one operator pair held out.
  arXiv  Detail & Related papers  (2026-03-17T18:02:28Z)
• Deterministic Fuzzy Triage for Legal Compliance Classification and Evidence Retrieval [0.0]
  Legal teams increasingly use machine learning to triage large volumes of contractual evidence.<n>Many models are opaque, non-deterministic, and difficult to align with frameworks such as HIPAA or NERC-CIP.<n>We study a simple, reproducible alternative based on deterministic dual encoders and transparent fuzzy triage bands.
  arXiv  Detail & Related papers  (2026-03-08T00:31:34Z)
• Symmetry-Constrained Language-Guided Program Synthesis for Discovering Governing Equations from Noisy and Partial Observations [0.0]
  We introduce SymLang (Symmetry-constrained Language-guided equation discovery), a unified framework that brings together three previously separate ideas.<n>Across 133 dynamical systems SymLang achieves an exact structural recovery rate of 83.7% under 10% observational noise.<n>In all tested regimes the framework correctly identifies structural degeneracy, reporting it explicitly rather than returning a confidently wrong single equation.
  arXiv  Detail & Related papers  (2026-03-06T20:42:36Z)
• Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol [69.11739400975445]
  We introduce the first theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents.<n>We show that cumulative distortion exhibits linear growth and high-probability deviations bounded by $O(sqrtT)$.<n>Key findings include: semantic weighting reduces distortion by 80%, and periodic re-grounding approximately every 9 steps suffices for error control.
  arXiv  Detail & Related papers  (2026-02-10T21:08:53Z)
• Transformers as Measure-Theoretic Associative Memory: A Statistical Perspective and Minimax Optimality [52.424255020469595]
  Transformers excel through content-addressable retrieval and the ability to exploit contexts, in principle, length.<n>We recast associative memory at the level of probability measures, treating a context as a distribution over unbounded tokens.<n>We show that a shallow measure-theoretic Transformer learns the recall-and-predict map under a spectral assumption on the input densities.
  arXiv  Detail & Related papers  (2026-02-02T09:34:17Z)
• Sparse Attention Post-Training for Mechanistic Interpretability [55.030850996535776]
  We introduce a simple post-training method that makes transformer attention sparse without sacrificing performance.<n>Applying a flexible sparsity regularisation under a constrained-loss objective, we show on models up to 1B parameters that it is possible to retain the original pretraining loss while reducing attention connectivity to $approx 0.3 % of its edges.
  arXiv  Detail & Related papers  (2025-12-05T16:40:08Z)
• STRIDE: Subset-Free Functional Decomposition for XAI in Tabular Settings [0.0]
  We present STRIDE, a framework that answers "what" features are important but fails to reveal "how" they interact.<n>We also introduce "component surgery", a diagnostic that isolates a learned interaction and quantifies its contribution.
  arXiv  Detail & Related papers  (2025-09-11T00:19:53Z)
• From Invariant Representations to Invariant Data: Provable Robustness to Spurious Correlations via Noisy Counterfactual Matching [14.679039741833305]
  Models that learn spurious correlations from training data often fail when deployed in new environments.<n>We propose a data-centric alternative that shifts the focus from learning invariant representations to leveraging invariant data pairs.<n>Noisy Counterfactual Matching improves robustness by leveraging even a small number of emphnoisy counterfactual pairs.
  arXiv  Detail & Related papers  (2025-05-30T17:42:32Z)
• BUMP: A Benchmark of Unfaithful Minimal Pairs for Meta-Evaluation of Faithfulness Metrics [70.52570641514146]
  We present a benchmark of unfaithful minimal pairs (BUMP) BUMP is a dataset of 889 human-written, minimally different summary pairs. Unlike non-pair-based datasets, BUMP can be used to measure the consistency of metrics.
  arXiv  Detail & Related papers  (2022-12-20T02:17:30Z)
• Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers [50.85524803885483]
  This work proposes a formal definition of statistically meaningful (SM) approximation which requires the approximating network to exhibit good statistical learnability. We study SM approximation for two function classes: circuits and Turing machines.
  arXiv  Detail & Related papers  (2021-07-28T04:28:55Z)
• Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors [52.06292503723978]
  Motivated by advances in compressive sensing with generative models, we study the problem of 1-bit compressive sensing with generative models. We first consider noiseless 1-bit measurements, and provide sample complexity bounds for approximate recovery under i.i.d.Gaussian measurements. We demonstrate that the Binary $epsilon$-Stable Embedding property, which characterizes the robustness of the reconstruction to measurement errors and noise, also holds for 1-bit compressive sensing with Lipschitz continuous generative models.
  arXiv  Detail & Related papers  (2020-02-05T09:44:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.