Hugging Face Trending Papers

Optimal Stabilizer Testing and Learning with Limited Quantum Memory

We study stabilizer state testing and learning with limited coherent quantum memory. Here an algorithm sequentially receives copies of an unknown $n$-qubit state, but may keep only $k$ qubits of coherent quantum memory between measurements.

Hugging Face Trending Papers
Jul 7

Provable learning separation for predicting time-evolution of quantum many-body systems

Given that quantum computers are naturally suited to simulate the behavior of quantum many-body systems, an immediate question arises: can one formulate physically motivated quantum machine learning (QML) tasks that exhibit learning separations? We address this problem by studying the learnability of quantum many-body dynamics from the perspective of probably approximately correct (PAC)-learning.

arXiv AI
Jul 8

Provable learning separation for predicting time-evolution of quantum many-body systems

arXiv:2607. 06472v1 Announce Type: cross Abstract: Given that quantum computers are naturally suited to simulate the behavior of quantum many-body systems, an immediate question arises: can one formulate physically motivated quantum machine learning (QML) tasks that exhibit learning separations?

By Rahul Bandyopadhyay, Riccardo Molteni, Jens Eisert, Vedran Dunjko, Sofiene Jerbi
arXiv Machine Learning
Aug 12

The Exact Replica Threshold for Nonlinear Moments of Quantum States

arXiv:2604. 22627v2 Announce Type: replace-cross Abstract: Joint measurements on multiple copies of a quantum state provide access to nonlinear observables such as $\operatorname{tr}(\rho^t)$, but whether replica number marks a sharp information-theoretic resource boundary has remained unclear.

By Shuai Zeng
arXiv Machine Learning
Jun 2

Quantum Reservoir Computing and Risk Bounds

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 Machine Learning
Jun 19

Optimal Ansatz-free Hamiltonian Learning In Situ

arXiv:2606. 19486v1 Announce Type: cross Abstract: Characterizing the features of a Hamiltonian that governs a quantum system serves as a fundamental subroutine of quantum device calibration, signal sensing, and error correction.

By Taiqi Zhou, Weiyuan Gong