mlsanityData to 7 Oct 2026

Tomography by Design: An Algebraic Approach to Low-Rank Quantum States

arXiv 2602.15202 ↗First posted 16 Feb 2026quant-ph cs.AI cs.NAcited by 1

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Abstract (arXiv)

We present an algebraic algorithm for quantum state tomography that leverages measurements of certain observables to estimate structured entries of the underlying density matrix. Under low-rank assumptions, the remaining entries can be obtained solely using standard numerical linear algebra operations. The proposed algebraic matrix completion framework applies to a broad class of generic, low-rank mixed quantum states and, compared with state-of-the-art methods, is computationally efficient while providing deterministic recovery guarantees.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 35 references that are in the map. First posted on arXiv 16 Feb 2026.

Builds on

5 of 35 references.

References · 5

  1. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 229
  2. 2015Dropping Convexity for Faster Semi-definite Optimizationcited by 40
  3. 2015A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completioncited by 13
  4. 2025Tensor Train Quantum State Tomography using Compressed Sensingcited by 5
  5. 2025Tensor Train Completion from Fiberwise Observations Along a Single Modecited by 1

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