2026· International Conference on Data Technologies and Applications· pp. 411-418· 0 citations· 23 references
Computer Science
TL;DR
This study evaluated state recognition from 100 by 100 two-gate current maps using 30 by 30 labeled patches under a strict device-level split to support fidelity-based quantum kernels as practical components for automated quantum dot tuning pipelines requiring few-shot generalization.
Abstract
: Semiconductor quantum dot platforms require rapid recognition of charge states during automated device tuning, especially when labeled data are scarce and device-to-device variation is strong. This study evaluated state recognition from 100 by 100 two-gate current maps using 30 by 30 labeled patches under a strict device-level split. Patch features were transformed with a signed logarithmic scale, standardized, compressed to four principal components, and scaled to the interval from zero to two pi. A fidelity quantum kernel support vector machine implemented in IBM Qiskit with a four-qubit ZZFeatureMap was compared against linear and radial basis function support vector machines across few-shot budgets of 10, 20, 40, and 80 samples per class. At 80 samples per class, the quantum kernel model achieved accuracy and macro F1 near 0.947 on held-out devices, outperforming the classical baselines in the evaluated setting. Noise-injection experiments showed stable macro F1 under perturbation. These findings support fidelity-based quantum kernels as practical components for automated quantum dot tuning pipelines requiring few-shot generalization.
These results demonstrate a scalable, reproducible workflow for quantum kernel experimentation at HEP-relevant scale, quantifying the practical cost of realistic hardware noise on quantum-enhanced classification.
Quantum Machine Learning (QML) combines quantum computing principles and traditional machine learning principles to provide new ways to solve difficult classification problems through the use of quantum technology. The goal of this paper is to implement a Quantum Kernel Support Vector Machine (QKSVM) using PennyLane and evaluate it against a standard image classification dataset, Fashion-MNIST. The Fashion-MNIST images are represented as 28x28 grayscale pixel arrays; they are normalized using a min-max normalizer with output values between zero and one. The 28x28 arrays are then dimensionally reduced to a size of four principal components (approximately 85% cumulative explained variance), and the QKSVM will utilize a quantum circuit limited to four qubits which is consistent with today's Noisy, Intermediate-Scale Quantum (NISQ) capabilities for performing simulations. Each input feature is represented as a qubit via an RY rotation and the qubits are then entangled using a CNOT circuit to generate the quantum kernel matrix, which is computed as the pairwise fidelity of each input image's state, which is subsequently used by a classical SVM classifier implementing a one-versus-all classification method. The QKSVM achieved approximately 40% accuracy and a macro-F1 score of 0.37 on a 100 sample stratified subset of the Fashion-MNIST test dataset, compared with 60% and 91.2%, respectively, using a classical Radial Basis Function Support Vector Machine (RBF-SVM) classifier with the same four principal components of input features and a Convolutional Neural Network (CNN) with full input images. With a primary bottleneck of qubit count, circuit depth, and shot noise, this work serves as a fully documented and reproducible baseline for future research on quantifying the performance of hybrid classical and quantum image classifiers across standardized image dataset benchmarks.
M. Devi, S.Sravanthi, V. Chaithanya et al.· 2026 7th International Confe...· 0 citations
Reliable attribution of artificial intelligence (AI)-generated text to a specific large language model (LLM) matters increasingly as LLMs proliferate, yet where quantum machine learning actually stands on this task has, to our knowledge, never been measured systematically. We benchmark the quantum support vector machine (QSVM) for binary attribution between Gemma 3 and Qwen 2.5 on a 5800-sample corpus from paired prompts: 83 configurations sweeping qubit count, regularization, training-set size, feature-map family, and circuit depth under exact, noiseless classical statevector simulation. QSVM validation accuracy plateaus at approximately 88%, whereas a classical support vector machine with a radial basis function kernel reaches approximately 97.8% on the identical fourteen-dimensional inputs: the ceiling belongs to the quantum (fidelity) kernel, not to the input representation. We measure the mechanism: off-diagonal quantum kernel values shrink exponentially with qubit count, the signature of exponential kernel concentration. The same classical model recovers the stylometric attribution fingerprint, showing it belongs to the shared feature pipeline rather than to the quantum kernel. All large-scale headline results generalize to an independent 1000-text test set produced after every design decision was frozen. The study provides a cautionary, reproducible benchmark for quantum kernel natural language processing and outlines an open-set extension as future work.
Kalin Kopanov, Tatiana V. Atanasova· Information· 0 citations
We introduce Quantum-KIP, a method that compresses a training set into a small set of kernel inducing points with soft labels. It uses a quantum feature map to compute state-fidelity overlaps and relies only on forward evaluations, avoiding backpropagation through quantum circuits. We provide a compression-induced stability analysis showing that replacing one training example changes the learned set and predictions by $O(m/n)$ . We further provide a joint analysis of this sensitivity bound with intrinsic quantum noise, showing how finite-shot measurement noise and depolarizing noise give rise to privacy-relevant distinguishability bounds for quantum-kernel observations. A circuit-execution analysis shows substantially fewer quantum runs than gradient-based approaches. On MNIST and CIFAR-10 datasets with six-qubit feature maps, Quantum-KIP achieves accuracy close to full-data training, large speedups, reduced privacy leakage, and robustness under depolarizing and measurement noise.
Baobao Song, Shiva Raj Pokhrel, Athanasios V. Vasilakos et al.· IEEE Transactions on Informa...· 0 citations
Gate-level noise degrades the classification accuracy of Quantum Support Vector Machines (QSVMs) on Noisy Intermediate-Scale Quantum (NISQ) hardware, and the degree of degradation depends on how classical data is encoded into quantum states. We tested Z, ZZ, a Pauli, and an amplitude-inspired feature maps under depolarizing, bit-flip, and phase-flip noise channels in $52$ controlled experiments with error probabilities $p =0.01, 0.05, 0.10$, and $0.50$. The amplitude-inspired feature map had $100$\% test accuracy up to $p = 0.10$ across all three noise channels, while other feature maps fell to $65$-$90$\% under the same noise level and type. The Z feature map was found to be immune to phase-flip noise to a significantly high error rate, a consequence of the commutation relation $[R_Z, Z] = 0$. Entangled circuit variants produced generalization gaps in train-test sets of up to $17.5$\% under noise, whereas the amplitude variants maintained zero gap throughout. These results give practitioners data-driven criteria for a feature map on near-term quantum hardware.
Muhammad Ahsan Shakeel, Saad Muzammil, Danyal Tayyub et al.· 0 citations
We present Tomography-NMR, an open-source Python package that reconstructs quantum density matrices from spectroscopic measurement data. The package implements a complete analysis pipeline for two-qubit quantum state tomography based on the product operator formalism: raw time-domain signals are Fourier-transformed into frequency-domain spectra, spectral peak intensities are mapped to expansion coefficients of the density matrix, and the full quantum state is reconstructed. Three integration methods are provided for different use cases: direct peak height measurement and fixed-parameter numerical integration require no theoretical reference and are suited to unknown states, achieving fidelities of approximately 98\% on a benchmark set of known states, while a systematic parameter optimization against a known target state achieves reconstruction fidelities exceeding 99\% for the same benchmark states. While the detailed theoretical framework for quantum state tomography is well established, the practical procedures for extracting density matrices from experimental spectra are often inadequately documented in the literature and obscured within proprietary software. This package addresses that gap by providing a fully transparent, reproducible implementation of every analysis step, from spectral preprocessing to density matrix visualization. The software has been validated on experimentally prepared two-qubit states measured via nuclear magnetic resonance (NMR) spectroscopy of coupled $^{31}$P nuclei. Average reconstruction fidelities range from 0.975 to 0.995 across a benchmark set of 20 two-qubit states, including the computational basis states, Bell states, and the outputs of three fundamental quantum gates (CNOT, H, and T). Although developed for NMR, the modular architecture facilitates adaptation to other spectroscopic platforms and alternative measurement protocols.
Jiwoo Seo, V. Mitrović· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
