arXiv Machine Learning
Jun 3
Spectral Anatomy of Quantum Gaussian Process Kernels
arXiv:2605. 30952v2 Announce Type: replace Abstract: Two recent results have reshaped quantum Gaussian processes (QGPs).
Effective Dimension Governs Generalization in Quantum Kernel Vision Models
arXiv:2606. 20183v1 Announce Type: new Abstract: Recent quantum vision models-quantum vision transformers and quantum convolutional networks-report two striking but unexplained empirical phenomena: (i) ansatze with more, or more uniformly distributed, entanglement generalize better, and (ii) injecting quantum noise can improve test accuracy rather than degrade it.
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arXiv Machine Learning
Jul 21
AQKA: Active Quantum Kernel Acquisition Under a Shot Budget
arXiv:2605. 14672v2 Announce Type: replace Abstract: Estimating an $N \times N$ quantum kernel from circuit fidelities requires $\Theta(N^2 S)$ measurement shots, the dominant bottleneck for deployment on near-term hardware.
arXiv Machine Learning
Jun 30
Active Quantum Kernel Acquisition for Gaussian Process Regression
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 Machine Learning
Jul 31
Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise
arXiv:2607. 25492v2 Announce Type: replace Abstract: We study stochastic optimization with heavy-tailed gradient noise.
arXiv Machine Learning
Jun 11
Bernstein-Schur Kernels: Random Features by Sketched Modulation and Radial Randomization
arXiv:2606. 11255v1 Announce Type: new Abstract: Bernstein--Schur kernels are products of a finite-feature kernel (one with an explicit finite-dimensional feature map) and a completely monotone shift-invariant kernel: nonstationary kernels that fall between the shift-invariant and dot-product templates random features usually exploit, so in general neither Bochner sampling nor polynomial sketching applies to the full kernel directly.
arXiv Machine Learning
Aug 5
The Signal Horizon: Local Blindness and the Contraction of Pauli-Weight Spectra in Noisy Quantum Encodings
arXiv:2602. 14735v2 Announce Type: replace-cross Abstract: The performance of quantum classifiers is typically analyzed through global state distinguishability or the trainability of variational models.
arXiv Machine Learning
Jun 24
Quantum Adaptive Self-Attention for Quantum Transformer Models
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.
arXiv Machine Learning
Jul 14
When cheap gradients fail: the measurement cost of attacking quantum classifiers
arXiv:2607. 11095v1 Announce Type: cross Abstract: Adversarial perturbations threaten machine learning classifiers, including variational quantum classifiers.
arXiv Machine Learning
Jul 8
Benign Overfitting with Quantum Kernels
arXiv:2503. 17020v2 Announce Type: replace-cross Abstract: Kernel methods compare inputs through feature maps.
arXiv Machine Learning
Jul 8
Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions
arXiv:2607. 06230v1 Announce Type: cross Abstract: Parameterized quantum circuits (PQCs) are increasingly used as policies and value functions in quantum reinforcement learning, yet it remains unclear when and why quantum policies generalize.
arXiv AI
Jul 1
A Coherence Law for Trainability in Noisy Equivariant Quantum Neural Networks
arXiv:2606. 30688v1 Announce Type: cross Abstract: Symmetry provides a quantum neural network structure, but on its own it does not keep the network trainable once noise is present.
arXiv Machine Learning
Jul 23
Statevector-Referenced Geometry Survival of a Four-Qubit ZZ Quantum Kernel on IBM Quantum Hardware: A Fixed-Subset Diagnostic Across Three Execution Configurations
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.