We propose a post-hoc explanation layer for binary Quantum Logic Decision Tree classifiers constructed with QLDT+. The layer does not modify the fitted tree, the decision threshold, or the predicted label. It translates the class-labelled QLDT+ paths into IF–THEN rules and attaches an explanation-based confidence score to each prediction. This score is the equal-weight average of three quantities: rule support, the activation mass of the contributing rules of the predicted class; path reliability, a class-specific reliability derived from the training evidence ratio; and the normalized distance of the QLDT+ score from the decision threshold. In a stratified 5-fold cross-validation on the Banknote and Diabetes datasets, which was repeated with ten random seeds, correct predictions received a higher mean score than incorrect ones in every run (mean difference 0.18 and 0.14), and near-threshold decisions received lower scores. In selective prediction, the error decreased as coverage decreased; on Diabetes, however, the decisions retained at 40% coverage or less were all predicted as class 0. The layer is therefore best read as a decomposable explanation of a fixed QLDT+ decision rather than as a calibrated probability or an optimized selective-prediction score.
We propose a post-hoc explanation layer for binary Quantum Logic Decision Tree classifiers constructed with QLDT+. The layer does not modify the fitted tree, the decision threshold, or the predicted label. It translates the class-labelled QLDT+ paths into IF–THEN rules and attaches an explanation-based confidence score to each prediction. This score is the equal-weight average of three quantities: rule support, the activation mass of the contributing rules of the predicted class; path reliability, a class-specific reliability derived from the training evidence ratio; and the normalized distance of the QLDT+ score from the decision threshold. In a stratified 5-fold cross-validation on the Banknote and Diabetes datasets, which was repeated with ten random seeds, correct predictions received a higher mean score than incorrect ones in every run (mean difference 0.18 and 0.14), and near-threshold decisions received lower scores. In selective prediction, the error decreased as coverage decreased; on Diabetes, however, the decisions retained at 40% coverage or less were all predicted as class 0. The layer is therefore best read as a decomposable explanation of a fixed QLDT+ decision rather than as a calibrated probability or an optimized selective-prediction score.
In this paper, we propose a post-hoc confidence- aware explanation layer for Quantum Logic Decision Trees (QLDTs) in binary classification. The goal is to improve the decision-level interpretability of QLDT+ predictions without modifying the underlying decision mechanism. The proposed layer translates class-labelled QLDT+ paths into linguistic IF– THEN rules and attaches an object-level explanation score to each prediction. This score is computed as a transparent aggregation of three interpretable components: rule-path acti- vation, empirical path reliability, and the normalized distance from the QLDT+ decision threshold. The resulting value is not intended to estimate a posterior class probability; rather, it serves as an explanation-based confidence indicator for decision support. Since the layer is applied only after the QLDT+ prediction has been made, the predicted labels remain fully determined by the original CQQL evaluation score and the global threshold. Experiments on the Banknote Authentication and Diabetes datasets show that correctly classified instances receive higher average explanation confidence than incorrectly classified ones, while near-threshold cases receive lower confi- dence values. Selective-prediction, component-wise ablation, and weight-sensitivity analyses further indicate that the proposed score provides useful information about decision stability and reliability while preserving the original QLDT+ classifier.
Quantum error correction can be enhanced by post-selecting out runs that are likely to produce a logical failure, but the most accurate measures for that require costly decoder-level information. We introduce a practical, decoder-agnostic post-selection method that learns directly from syndrome data. The method trains a supervised classifier to distinguish between syndromes from low- and high-noise regimes, and then uses the classifier's output as an abort score for new runs, without requiring logical-error labels, correction operators, or code-specific likelihood calculations. We validate the approach in three complementary settings: circuit-level simulations of the Gross bivariate-bicycle code, code-capacity simulations of the surface code, and experimental logical magic-state distillation data from the QuEra neutral-atom processor. In the Gross and surface codes, learned syndrome post-selection reduces the conditional logical error rate at a fixed acceptance rate, with performance comparable to syndrome-weight filtering. For the surface code, the learned classifier reveals a post-selection transition distinct from the conventional decoding threshold. In the experimental data, the machine-learning score outperforms syndrome-weight post-selection and, when combined with logical-gap filtering, improves the output fidelity beyond using the logical gap alone. These results show that syndrome-only learning provides a scalable and hardware-compatible route to improving the reliability of quantum error correction.
The quantum-logic inspired decision tree (QLDT) is based on quantum logic concepts and input values from the unit interval whereas the traditional decision tree is based on Boolean values. The logic behind the QLDT obeys the rules of a Boolean algebra. The QLDT is appropriate for classification problems where for a class decision several input values interact gradually with each other. The QLDT construction for a classification problem with n input attributes requires the computation of 2n minterms. The QLDT+ method, however, uses a heuristic for obtaining a QLDT with much smaller computational complexity. As result, the QLDT+ method can be applied to classification problems with a higher number of input attributes.
Abstract: This study investigates an explainable quantum-logical multi-agent system (QL-MAS) for autonomous scientific discovery in high-dimensional research landscapes. The central methodological problem is that modern discovery settings combine large numbers of variables, heterogeneous evidence types, and partially conflicting reasoning signals, yet conventional single-model pipelines often compress this complexity into opaque rankings. The proposed framework addresses this limitation by treating candidate scientific hypotheses as quantum-logical superpositions of evidence generated by three complementary agents: an evidence agent based on mutual information, a structure agent based on principal-component loadings, and a rule agent based on interpretable decision structures. The resulting architecture does not claim physical quantum computation; rather, it uses quantum-logical amplitude composition as a mathematically disciplined way to represent concurrent support, interference, and consensus across agents. Empirical evaluation is conducted on three real public datasets that approximate heterogeneous scientific discovery landscapes: the Breast Cancer Wisconsin (Diagnostic) dataset (569 observations, 30 variables), the Wine Recognition dataset (178 observations, 13 variables), and the Diabetes dataset used in the least-angle-regression literature (442 observations, 10 baseline variables). These corpora were selected because they are non-synthetic, scientifically grounded, and sufficiently high-dimensional to test feature prioritization, interpretability, and cross-domain transfer. Across the three datasets, the proposed system identifies compact sets of high-salience hypotheses while preserving strong downstream predictive utility. The top-five features selected by QL-MAS attain cross-validated scores of 0.965 on breast cancer classification, 0.955 on wine classification, and 0.465 R² on diabetes progression, remaining close to full-feature baselines while offering substantially more interpretable evidence paths. The results indicate that explainable multi-agent discovery is strongest when high predictive relevance and rule-based consistency converge, and weakest when latent structure is diffuse or poorly aligned with supervised objectives. The analysis therefore supports the conclusion that autonomous discovery systems in research environments should not be assessed solely by predictive accuracy, but by the transparency of their evidential decomposition and by the stability of agreement across reasoning agents. The paper contributes a theoretically coherent framework, a real-data evaluation protocol, and a discussion of limitations, governance, and future directions for autonomous scientific reasoning systems. Keywords: autonomous scientific discovery; explainable AI; multi-agent systems; quantum logic; high-dimensional data; interpretable machine learning; research landscapes
We propose a Trace-distance binary Tree AdaBoost (TTA) multi-class quantum classifier, a practical pipeline for quantum multi-class classification that combines quantum-aware reductions with ensemble learning to improve trainability and resource efficiency. TTA builds a hierarchical binary tree by choosing, at each internal node, the bipartition that maximizes the trace distance between average quantum states; each node trains a binary AdaBoost ensemble of shallow variational quantum base learners. By confining intrinsically hard, small trace distance distinctions to small node-specific datasets and combining weak shallow learners via AdaBoost, TTA distributes capacity across many small submodels rather than one deep circuit, mitigating barren-plateau and optimization failures without sacrificing generalization. Empirically TTA achieves top test accuracy ($\approx $100\%) among quantum and classical baselines, is robust to common quantum errors, and realizes aggregate systems with 10000 cumulative layers and 0.2M parameters, implemented as many shallow circuits. Our results are empirical and implementable on near-term platforms, providing a resource-efficient route to scalable multi-class quantum machine learning.