mlsanityData to 7 Oct 2026

Certifying almost all quantum states with few single-qubit measurements

arXiv 2404.07281 ↗First posted 10 Apr 2024quant-ph cs.IT cs.LGcited by 7

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Abstract (arXiv)

Certifying that an n-qubit state synthesized in the lab is close to the target state is a fundamental task in quantum information science. However, existing rigorous protocols either require deep quantum circuits or exponentially many single-qubit measurements. In this work, we prove that almost all n-qubit target states, including those with exponential circuit complexity, can be certified from only O(n^2) single-qubit measurements. This result is established by a new technique that relates certification to the mixing time of a random walk. Our protocol has applications for benchmarking quantum systems, for optimizing quantum circuits to generate a desired target state, and for learning and verifying neural networks, tensor networks, and various other representations of quantum states using only single-qubit measurements. We show that such verified representations can be used to efficiently predict highly non-local properties that would otherwise require an exponential number of measurements. We demonstrate these applications in numerical experiments with up to 120 qubits, and observe advantage over existing methods such as cross-entropy benchmarking (XEB).

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 18 of its 62 references that are in the map. First posted on arXiv 10 Apr 2024.

Builds on

18 of 62 references.

References · 18

  1. 2018Barren plateaus in quantum neural network training landscapescited by 624
  2. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 229
  3. 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 195
  4. 2019Deep autoregressive models for the efficient variational simulation of many-body quantum systemscited by 64
  5. 2016Efficient quantum tomography IIcited by 54
  6. 2021Exponential separations between learning with and without quantum memorycited by 50
  7. 2022Out-of-distribution generalization for learning quantum dynamicscited by 49
  8. 2021Neural tensor contractions and the expressive power of deep neural quantum statescited by 21
  9. 2024Learning shallow quantum circuitscited by 20
  10. 2017Quantum state certificationcited by 13
  11. 2022From Tensor Network Quantum States to Tensorial Recurrent Neural Networkscited by 13
  12. 2021Toward Instance-Optimal State Certification With Incoherent Measurementscited by 12
6 more
  1. 2023The power and limitations of learning quantum dynamics incoherentlycited by 12
  2. 2022Tight Bounds for Quantum State Certification with Incoherent Measurementscited by 11
  3. 2019(Pseudo) Random Quantum States with Binary Phasecited by 10
  4. 2020Fast and robust quantum state tomography from few basis measurementscited by 10
  5. 2023Empirical Sample Complexity of Neural Network Mixed State Reconstructioncited by 4
  6. 2014Uniform multicommodity flow in the hypercube with random edge capacitiescited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 1.

7 papers.