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).
Certification cost of quantum models: measurement correlation, not parameter count
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arXiv Machine Learning
Sep 22
The Price of Self-Calibration: Exact Evidence Budgets and Manufactured Blind Sets in Adaptive Monitoring
The paper derives precise cost formulas for self‑calibrating monitors that adjust thresholds online to maintain a specified long‑run false‑alarm rate under arbitrary drift. It shows that the guarantee is an accounting identity, independent of the monitored signal, and provides exact evidence identities for both step and ramp drift scenarios, as well as an exact law for the fluctuation of the certificate’s own alarm rate. Additionally, it proves that any monitor designed to tolerate a drift class is blind to all faults in the difference of that class, identifying the blind set for speed‑bounded drift classes and quantifying power outside this set with a sharp Gaussian projection bound.
arXiv Machine Learning
Jun 19
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.
arXiv Machine Learning
Sep 17
QEMScore: How Much Does the Measurement Add to Learned Quantum Error Mitigation?
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 AI
Aug 18
Pricing the Risk of Runtime Compression: Anytime-Valid Admission and a Served-Output Law for Compressed Serving State
arXiv:2608. 15810v1 Announce Type: new Abstract: Runtime compression of serving state trades quality for capacity with no priced guarantee: systems adapt precision on load signals with no soundness statement, and certified approaches budget request-level risk by a union bound over a pre-declared event count.
arXiv AI
4d ago
Quantization Error Is Spectrally Flat: A Single Random Probe Is a Calibrated, Data-Free Sensitivity Estimator, with Application to Budget-Targeted Mixed-Precision Quantization
arXiv:2609.33923v2 Announce Type: replace-cross Abstract: A single random Gaussian probe gives an unbiased estimate of the squared Frobenius norm of a layer's quantization error. The estimator is wel...
arXiv Machine Learning
Sep 10
The Oversight Gap: What LLM Safety Monitors Miss, and Why It Is Not Capability
arXiv:2609.07162v1 Announce Type: new Abstract: Several properties safety monitors are asked to certify, among them cross-tenant noninterference, sandbagging and evaluation awareness, are 2-safety hy...
arXiv Machine Learning
Aug 19
Dynamic Entanglement-Weighted Pruning for Quantum Federated Unlearning in Supply-Chain Risk Prediction
The paper proposes Entanglement-Weighted Pruning (EWP), a method for unlearning a client’s contribution from a federated quantum classifier without retraining from scratch. EWP scores each trainable circuit parameter by combining a Fisher‑information estimate on the target client’s data with a structural entanglement weight, pruning the lowest‑scoring parameters and optionally fine‑tuning the remaining ones. Experiments on a four‑qubit data‑re‑uploading ansatz trained with FedAvg across five simulated supply‑chain‑risk clients show that EWP achieves accuracy comparable to full retraining while reducing forgetting and wall‑clock time by about sixteenfold, outperforming random, Fisher‑only, or entanglement‑only pruning.
arXiv Machine Learning
Jul 14
An End-to-End Hybrid Quantum--Classical Sampling Workflow for Discrete Markov Random Fields: A Reproducible Case Study
arXiv:2607. 09893v1 Announce Type: cross Abstract: Sampling from discrete Markov random fields (MRFs) is a hard problem.
arXiv Machine Learning
Jul 14
Depth-Efficient Quantum Topological Data Analysis for Regime-Specific Detection of Financial Stress
arXiv:2607. 09906v1 Announce Type: cross Abstract: We present, to our knowledge, the first adaptation of Pauli Correlation Encoding (PCE) to quantum topological data analysis, reformulating Betti number estimation as a depth-efficient variational optimization over a compressed qubit register.
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 11
Quantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning
arXiv:2606. 12211v1 Announce Type: cross Abstract: A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest.