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.
arXiv:2607. 20377v1 Announce Type: cross Abstract: Quantum-kernel methods encode a dataset's geometry in a Gram matrix, so learning claims on hardware kernels assume the intended geometry survives execution.
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.
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...
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.
arXiv:2609.15838v1 Announce Type: cross Abstract: Per-matrix singular value decomposition (SVD) truncation is Eckart-Young optimal in the whitened Frobenius norm, but errors from independently compre...
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.
arXiv:2607. 05814v1 Announce Type: cross Abstract: Real-time decoding is a major bottleneck in scaling quantum error correction (QEC) from noisy intermediate-scale quantum (NISQ) devices to fault-tolerant quantum computing.
arXiv:2605. 30358v2 Announce Type: replace Abstract: Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, with performance constrained by noise.
arXiv:2608. 06846v1 Announce Type: cross Abstract: We test whether a parameterized quantum circuit (PQC) improves a hybrid quantum-classical model's performance on classical datasets, using an interface-matched classical map as the control while holding all other components fixed.
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.
The paper introduces QXymb, a framework for building observational declarative twins of quantum circuits, and presents its first complete order‑0 specialization, QILP‑0. QILP‑0 transforms observed circuit behavior into a finite multi‑valued propositional logic program by incrementally traversing a declared family of quantum observables, quantifying progress via reference‑relative coverage, and preserving observational semantics through deterministic mapping back to original observable columns. Validation on Bars & Stripes and MNIST quantum machine learning settings shows that the induced QILP‑0 theory achieves perfect, conflict‑free reconstruction of the discrete relations, with logical exactness separated from numerical and discretization uncertainties. whyItMatters":"The work demonstrates a method to construct exact observational declarative twins of quantum circuits, enabling precise logical reconstruction of quantum behavior independent of numerical uncertainties."
arXiv:2607. 20943v1 Announce Type: cross Abstract: Quantum Phase Estimation (QPE) is a foundational algorithm for molecular ground-state energy estimation, but its deep circuit requirements make direct hardware execution impractical on Noisy Intermediate-Scale Quantum (NISQ) devices.