arXiv:2501. 08640v2 Announce Type: replace Abstract: We propose a way to bound the generalisation errors of several classes of quantum reservoirs using the Rademacher complexity.
By Naomi Mona Chmielewski (L2S), Nina Amini (L2S, CNRS), Joseph Mikael
arXiv:2605. 03573v3 Announce Type: replace-cross Abstract: Quantum machine learning increasingly relies on pure-state representations, motivating generative models that sample directly in quantum representation space rather than perturbing classical inputs and re-encoding.
By Jian Xu, Wei Chen, Shigui Li, Chao Li, Jingyuan Zheng, Delu Zeng, John Paisley, Qibin Zhao
arXiv:2606. 30358v1 Announce Type: cross Abstract: We design an algorithm for learning the coefficients of an $n$-qubit constant-local Lindbladian to $\varepsilon$ error with $O(g d^2 \log(n) / \varepsilon^2)$ total evolution time, where $g$ is the single-site energy and $d$ is the (approximate) degree of the interaction graph.
By Laura Lewis, Ewin Tang, John Wright
arXiv:2512. 06695v3 Announce Type: replace Abstract: Quantum generative models exploit quantum superposition and entanglement to enhance learning efficiency for both classical and quantum data.
By Haipeng Cao, Kaining Zhang, Dacheng Tao, Zhaofeng Su
arXiv:2410. 21258v2 Announce Type: replace-cross Abstract: Topological data analysis (TDA) aims to extract noise-robust features from a data set by examining the number and persistence of holes in its topology.
By Casper Gyurik, Alexander Schmidhuber, Robbie King, Vedran Dunjko, Ryu Hayakawa
arXiv:2602. 20293v3 Announce Type: replace Abstract: We study a discrete denoising diffusion framework that integrates a sample-efficient estimator of single-site conditionals with round-robin noising and denoising dynamics for generative modeling over discrete state spaces.
By Karthik Elamvazhuthi, Abhijith Jayakumar, Andrey Y. Lokhov