arXiv:2606. 06316v1 Announce Type: cross Abstract: Financial crashes, cascading failures in infrastructure, and critical errors in AI systems are frequently triggered by events that occur with extremely small probability.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2610.09635v1 Announce Type: cross Abstract: Anomaly detection on small and unbalanced datasets remains very challenging in machine learning, although this scenario is common in several domains,...
The paper demonstrates that a small quantum computer of polylogarithmic size can achieve large‑scale classification and dimension reduction on massive classical data, while any classical machine achieving the same performance would need exponentially larger size. It shows that classical machines even if exponentially larger still require superpolynomially more samples and time. The authors provide evidence from real‑world applications such as single‑cell RNA sequencing and movie review sentiment analysis, achieving four to six orders of magnitude reduction in size with fewer than 60 logical qubits.
The paper introduces a classical algorithm that dequantizes a quantum sampler used for learning with optimized random features. By sampling heavy indices and reducing the transformation to a small principal block, the method produces a sparse classical representation with operator‑norm guarantees. This approach enables a classical sampler with specified accuracy and polynomial runtime, demonstrating that quantum block‑encoding factorizations can provide sufficient classical structure even when direct sampling access to the composite matrix is unavailable.
arXiv:2607. 21409v1 Announce Type: cross Abstract: A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs).
The paper investigates online learning of quantum states using logarithmic loss, a quantum analogue of online portfolio selection. It establishes a regret lower bound, proving that existing algorithms like Exponential Weights and Tseng et al. (2025) achieve optimal polynomial dependence on dimension. Leveraging this lower bound, the authors present a sample‑optimal quantum state tomography algorithm (up to polylogarithmic factors) and introduce a method for constructing confidence sequences for matrix‑valued random variables, with numerical results indicating competitive performance.
arXiv:2607. 20302v1 Announce Type: new Abstract: Quantum machine learning (QML) algorithms in high energy physics (HEP) can efficiently represent and leverage long-range, high-order correlations in high-dimensional collider data, potentially with fewer parameters and favorable scaling relative to classical models.