The study reports on three separate executions of a four‑qubit ZZ feature map on the ibm_fez backend, each using 1024 shots per circuit. The authors compare the resulting hardware‑reconstructed Gram matrices to an exact statevector reference, finding off‑diagonal RMSE values of 0.0878, 0.0864, and 0.0427 for the baseline, dynamical decoupling, and gate‑twirling jobs respectively, and centered kernel alignment scores ranging from 0.933 to 0.989. The gate‑twirling job shows the smallest deviation across all metrics, while the baseline job’s contrasts remain stable under deletion‑stable diagnostics; the study notes that sampling alone cannot explain the observed errors and that implementation fidelity and task relevance are distinct diagnostic axes.
By Rostyslav Sipakov
arXiv:2609.00475v1 Announce Type: cross
Abstract: Angle-encoded quantum kernels on tabular data collapse when the feature map is wider than the intrinsic dimension of the data. We propose the correla...
By Ana Paula Appel
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
By Jonas J\"ager, Philipp Els\"asser, Elham Torabian
The paper investigates how postprocessing routines in quantum neural network software can cause significant data loss when run on large quantum hardware. In a case study of Qiskit’s “SamplerQNN”, a filter that assumes measurement bit‑strings are in virtual qubit space removed 85–99.6% of valid shots on IBM backends, leading to unnormalised probability vectors and distorted predictions. This loss caused inference accuracy to drop from 0.94 to 0.39 and compressed training loss signals by 22–27×, severely reducing optimizer sensitivity. The authors implemented a layout‑based marginalisation fix that was merged into the library to make “SamplerQNN” forward‑compatible with current and future hardware.
By Soraya V. Panambalom, Edoardo Altamura, Nick Chancellor, Jonte R. Hance
arXiv:2607. 10707v1 Announce Type: cross Abstract: We propose a unified meta-decoding framework for quantum error correction that learns syndrome-to-recovery mappings across multiple stabilizer codes and noise settings, without requiring separate decoders for each configuration.
By Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique, Rajeev Singh
arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it.
By Chi-Sheng Chen, En-Jui Kuo
arXiv:2607. 19782v1 Announce Type: cross Abstract: Kernel methods are powerful tools in machine learning but commonly used full-Gram kernels face three key limitations: (1) quadratic scaling with training set size; (2) the use of fixed, non-trainable kernels; and (3) the absence of an intrinsic formulation for multiclass classification.
By Kilian Tscharke, Pascal Debus
arXiv:2605. 30952v2 Announce Type: replace Abstract: Two recent results have reshaped quantum Gaussian processes (QGPs).
By Jian Xu, Chao Li, Guang Lin, Yuning Qiu, Delu Zeng, John Paisley, Qibin Zhao
The paper investigates how postprocessing routines in quantum neural network software can inadvertently discard a large portion of valid measurement data when run on real quantum hardware. In a case study of Qiskit’s “SamplerQNN”, a filter that assumes virtual qubit space caused 85–99.6% of measurement shots to be lost on IBM backends, leading to unnormalised probability vectors, degraded inference accuracy (from 0.94 to 0.39), and a 22–27× compression of the training loss signal. The authors provide a layout‑based marginalisation fix that has been merged into the library to ensure forward‑compatibility with current and future hardware.
The paper introduces QEMScore, a metric that compares learned quantum error mitigators to capacity‑matched controls that do not use measurement data. Using simulated circuits with exact ideal answers, the study finds that many mitigators gain little from measurement inputs, with a plain polynomial model often outperforming them. On real hardware data, however, measurement inputs can provide predictive benefits, highlighting that performance depends on representation and protocol specifics.
By Yue Zhao, Huayue Gu, Yushun Dong, Xiyang Hu
arXiv:2606. 28833v1 Announce Type: new Abstract: Quantum kernel estimation on near-term hardware is shot-budgeted: every entry of the kernel Gram matrix is a Bernoulli expectation that must be sampled with a finite number of circuit executions.
By Jian Xu, Delu Zeng, Qibin Zhao
arXiv:2604. 23931v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are a leading approach to quantum machine learning on near-term devices, yet it remains unclear which circuit architecture yields the best accuracy-parameter trade-off on classical tabular data.
By Chi-Sheng Chen, En-Jui Kuo