Data & Analytics

Classical Shadow Estimation of Unitary Channels (CSEU) — Heisenberg-limited prediction of quantum evolution properties without full tomography.

Spectral analysis of quantum circuits using Circuit Harmonic Matrices. Predict quantum machine learning model performance from circuit architecture without training. Analyze circuit expressivity, trainability, and generalization capacity via frequency-domain methods. Activation: quantum circuit spectral, circuit harmonic matrix, quantum circuit analysis, QML spectral, quantum model expressivity, circuit eigenvalue, quantum neural network spectrum.

Quantum-inspired neural network for vision-brain understanding using voxel controlling, phase shifting, and measurement-like projection in Hilbert space. Maps brain region connectivity via quantum-inspired modules for fMRI analysis. Use when: (1) analyzing fMRI voxel connectivity, (2) building vision-brain decoding models, (3) reconstructing images from brain signals, (4) designing quantum-inspired architectures for neuroimaging. Activation: quantum brain, vision-brain understanding, voxel co...

Quantum Boltzmann Machine via Bilevel Optimization methodology. Extends QAOA circuit to bilevel optimization for fully connected QBMs, overcoming the fixed target Hamiltonian barrier. Use when building quantum generative models, training quantum Boltzmann machines, or extending QAOA for ML applications. arXiv:2605.07473

Quantum block encoding methodology for Difference-of-Gaussian (DoG) operators on periodic grids. Implements Linear Combination of Unitaries (LCU) framework without black-box oracles. Activation: quantum block encoding, DoG operator, quantum machine learning, quantum signal processing.

Quantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI. Uses angle encoding, variational encoder-decoder with trash qubits, and incompressibility-based anomaly scoring. Achieves ROC-AUC ~0.95 slice-level and ~0.813 patch-level with spatially localized anomaly heatmaps. Use when: quantum anomaly detection, brain MRI analysis, quantum autoencoder design, compression-based medical diagnostics, trash qubit encoding, variational quantum encoders.

Quantum convolutional autoencoder (QCAE) for reconstruction-based anomaly detection using QCNN architectures - semi-supervised training on normal samples with reconstruction error as anomaly score.

Quantum annealing-based feature selection for interpretable AI in Convolutional Neural Networks. Uses constrained optimization to select most important feature maps contributing to predictions, providing explainable AI with improved class disentanglement. Use when implementing XAI for CNNs, quantum annealing feature selection, or model interpretation via quantum computing.

Quantum adversarial defense methodology using quantum autoencoders for protecting quantum classifiers against adversarial perturbations. Covers quantum autoencoder purification, adversarial training-free defense frameworks, confidence metrics for adversarial sample detection, and evaluation of variational quantum classifiers under attack. Use when defending QML models, analyzing quantum adversarial robustness, implementing purification-based defenses, or studying adversarial attacks on variat...

Quantum Machine Learning methodology for 6G edge network adaptive communication and model aggregation in V2X systems. Combines quantum ML with edge computing for efficient vehicular communication, model collaboration, and generalization. Use when: (1) designing 6G quantum-enhanced networks, (2) V2X communication optimization, (3) edge AI model aggregation, (4) quantum ML for communication systems, (5) adaptive quantum edge networks.

Quantized time statistics methodology for quantum walks under weak rank-K measurements using topological winding numbers

Mutation testing framework for Quantum Neural Networks (QNNs) based on the QuanForge methodology (arXiv:2604.20706). Use this skill when testing QNN robustness, analyzing quantum circuit vulnerabilities, performing mutation testing on quantum ML models, localizing weak regions in quantum circuits, or comparing QNN test suites. Also triggered by keywords: quantum testing, QNN testing, mutation testing, 量子测试, 量子神经网络测试.

Scalable on-hardware training of Quantum Neural Networks for clinical data imputation methodology - demonstrates practical quantum machine learning for handling missing data in clinical datasets.

SPATE: Spiking-Phase Adaptive Temporal Encoding for Quantum Machine Learning. Bridges neuromorphic computing with QML via spike-based temporal encoding into phase-encoded qubits. Use when: spiking quantum encoding, QML temporal encoding, spike encoding quantum, neuromorphic quantum computing, temporal data for QML, 脉冲量子编码.

Quantum Machine Learning model testing and robustness analysis methodology. Covers mutation testing for QNN circuits, accuracy/robustness evaluation of Variational Quantum Circuits (VQCs), and practical considerations for deploying QML models on NISQ-era quantum hardware. Use when: (1) testing quantum neural network implementations for correctness, (2) evaluating QML model robustness against circuit faults and noise, (3) designing test suites for parametrized quantum circuits, (4) analyzing V...

Design framework-agnostic quantum machine learning (QML) systems using the Model-Agnostic Learning System (MALS) paradigm. Extracts QML models from any framework (PennyLane, Qiskit, TensorFlow Quantum, etc.) into portable representations with auto-validation and cross-framework compatibility testing.

Quantum Machine Learning feature encoding methodology — three-axis cost-expressivity-robustness taxonomy, depth-fidelity bounds under NISQ decoherence, unified trainability analysis, and five-regime decision framework for selecting encoding strategies on real hardware.

Quantum Machine Learning expressivity separation methodology. Based on Anschuetz & Gao (Quantum 10, 1976, 2026). Provides framework for constructing efficiently trainable QNNs with provable polynomial memory separations over classical neural networks. Use when: (1) designing QNN architectures with provable quantum advantage, (2) analyzing expressivity vs trainability trade-offs, (3) implementing quantum contextuality as computational resource, (4) comparing quantum vs classical sequence model...

Quantum Machine Learning with Equilibrium Propagation for medical image analysis. Energy-based training without backpropagation using Variational Quantum Circuits (VQCs) for resource-constrained quantum hardware. Use when: analyzing blood cells, leukemia detection, medical imaging with QML, energy-based quantum training, backprop-free quantum networks, or evaluating QML feasibility on NISQ devices.

Unsupervised clustering via steady-state quantum transport in open quantum networks (GKSL master equation). Encodes data as input states and infers cluster assignments from terminal current observables - no full state tomography required. Use when: quantum clustering, GKSL transport, analog quantum ML, open quantum network clustering, Qlustering algorithm, steady-state quantum transport clustering, tomography-free quantum learning, quantum unsupervised learning, algorithm-hardware co-design c...

QDiffusion-TS - First quantum generative diffusion model for time series synthesis with real quantum hardware validation on IQM processor

Hybrid quantum-classical architecture combining path signature kernels with QCNN for time series classification, addressing time reparameterization invariance. (arXiv: 2607.07634)

Parallel multi-circuit quantum feature fusion methodology for medical image classification. Use when: (1) building hybrid quantum-classical CNN architectures for biomedical image classification, (2) comparing quantum vs classical models with statistical rigor (Wilcoxon signed-rank test, Cohen's d effect size), (3) designing parallel quantum encoding circuits (amplitude + angle encoding simultaneously), (4) parameter-matched fairness evaluation for QML vs classical baselines. Covers QCNN archi...

多目标量子工作流优化方法论。系统化选择 NISQ 设备上的编译策略、噪声抑制和误差缓解方案。基于 QBalance 框架，涵盖加权目标函数、非支配选择规则、生存乘积误差代理、贝叶斯候选排序和分布诊断。Activation: qbalance, quantum workflow, quantum compilation optimization, NISQ error mitigation, quantum noise suppression, multi-objective quantum strategy.