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
The paper introduces a Projected Riemannian Gradient Descent (RGD) algorithm for computing the Bures‑Wasserstein barycenter of positive definite matrices, achieving dimension‑independent linear convergence at unit step size. It resolves a previous dichotomy by showing that clipping eigenvalues to a fixed interval yields a closed‑form, non‑expansive projection in the BW metric, allowing the algorithm to match the empirical speed of unit‑step RGD while maintaining theoretical guarantees. The method also extends to the invariant matrix projection problem, providing a unified dimension‑independent analysis.
arXiv:2608.23094v1 Announce Type: new
Abstract: One implicit DDIM inversion step is the cheapest probe of whether a pretrained diffusion model encodes local manifold geometry. It is the stationarity...
By Gordei Verbii
arXiv:2609. 03762v1 Announce Type: new Abstract: The computation of the Bures-Wasserstein (BW) barycenter of an ensemble of positive definite matrices arises throughout machine learning, optimal transport, and quantum information.
By A. Afham
arXiv:2605. 13268v2 Announce Type: replace-cross Abstract: Trotter Suzuki product formulas are the standard route to Hamiltonian evolution on noisy intermediate-scale quantum (\NISQ{}) hardware, but their accuracy depends on three coupled choices: term grouping, product-formula order, and time-step allocation.
By WenBin Yan
The paper introduces a quantum score‑matching framework that extends classical score matching to quantum states, addressing challenges posed by noncommuting density operators. It demonstrates that this method can learn thermal (Gibbs) states without extra state preparation, achieving optimal sample complexity in high‑temperature regimes for local Hamiltonians. Numerical tests and experiments on IBM quantum hardware confirm the approach’s effectiveness and NISQ‑friendly performance, reducing Hamiltonian‑parameter error from 64% to about 10%.
By Yulong Dong, Jiaqi Leng