arXiv Machine Learning By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao

Fixed-Protocol Amortized MPS Tomography with Conformalized Predictive Uncertainty

Read the original on arXiv Machine Learning →

arXiv:2607. 11273v1 Announce Type: cross Abstract: Quantum state tomography is sample-starved, and the states one prepares live on a narrow, learnable manifold.

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 Machine Learning.

arXiv Machine Learning
Aug 21

Quantum Gaussian processes for prediction of channel observations

arXiv:2608. 19306v1 Announce Type: cross Abstract: Given a set of input states, we consider the task of predicting the expectation value of a Pauli observable at the output of an unknown quantum evolution, using only a limited number of measurements.

By Jonas J\"ager, Yaroslav Khmelnitskiy, Paolo Braccia, Artur Miroszewski, Diego Garc\'ia-Mart\'in, M. Cerezo, Piotr Czarnik
arXiv Machine Learning
Aug 13

Generative Learning for Quantum Measurement Design

arXiv:2608. 11396v1 Announce Type: cross Abstract: Extracting quantum information from a quantum state is a fundamental task of quantum computation, often requiring the estimation of many non-commuting observables under a finite measurement budget.

By Jun Dai, Olivier Nahman-L\'{e}vesque, Guillaume Rabusseau, Hong-Ye Hu, Cunlu Zhou
arXiv Machine Learning
Sep 22

Watching Quantum Models Think: Hilbert-Space Interpretability in Quantum Transformer Blocks

The paper demonstrates that quantum transformer blocks can be intrinsically interpretable by tracking quantum mutual information, entanglement entropy, and state fidelity across layers. Experiments on four synthetic tasks show that learned mutual information aligns with task structure, entanglement is essential for accuracy, and mutual information predicts prediction correctness. These findings are validated on IBM Quantum hardware, illustrating that quantum computation’s physics can provide observable interpretability signals.

By Diego Iacopetta, Andrea Gasparini
Hugging Face Trending Papers
Aug 11

Generative Learning for Quantum Measurement Design

Extracting quantum information from a quantum state is a fundamental task of quantum computation, often requiring the estimation of many non-commuting observables under a finite measurement budget. For both near-term and early fault-tolerant settings, the measurement protocol must balance statistical efficiency against implementation resources such as circuit depth, connectivity, and entangling-gate count.