Certifying almost all quantum states with few single-qubit measurements
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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
- 2018Barren plateaus in quantum neural network training landscapescited by 624
- 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 229
- 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 195
- 2019Deep autoregressive models for the efficient variational simulation of many-body quantum systemscited by 64
- 2016Efficient quantum tomography IIcited by 54
- 2021Exponential separations between learning with and without quantum memorycited by 50
- 2022Out-of-distribution generalization for learning quantum dynamicscited by 49
- 2021Neural tensor contractions and the expressive power of deep neural quantum statescited by 21
- 2024Learning shallow quantum circuitscited by 20
- 2017Quantum state certificationcited by 13
- 2022From Tensor Network Quantum States to Tensorial Recurrent Neural Networkscited by 13
- 2021Toward Instance-Optimal State Certification With Incoherent Measurementscited by 12
6 more
- 2023The power and limitations of learning quantum dynamics incoherentlycited by 12
- 2022Tight Bounds for Quantum State Certification with Incoherent Measurementscited by 11
- 2019(Pseudo) Random Quantum States with Binary Phasecited by 10
- 2020Fast and robust quantum state tomography from few basis measurementscited by 10
- 2023Empirical Sample Complexity of Neural Network Mixed State Reconstructioncited by 4
- 2014Uniform multicommodity flow in the hypercube with random edge capacitiescited by 1
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Citing papers per year: 2024: 4; 2025: 2; 2026: 1.
7 papers.
- 2024When can classical neural networks represent quantum states?cited by 6
- 2026TCS-BENCH: Benchmarking State-of-the-Art Generative AI Theoretical Computer Science Research Abilitycited by 3
- 2024Learning topological states from randomized measurements using variational tensor network tomographycited by 2
- 2024Learning to Classify Quantum Phases of Matter with a Few Measurementscited by 2
- 2025Instance-Optimal Quantum State Certification with Entangled Measurementscited by 1
- 2024Hybrid Classical-Quantum architecture for vectorised image classification of hand-written sketchescited by 0
- 2025Non-iid hypothesis testing: from classical to quantumcited by 0
- 2026TCS-BENCH: Benchmarking State-of-the-Art Generative AI Theoretical Computer Science Research Ability10 Aug 2026
- 2025Non-iid hypothesis testing: from classical to quantum7 Oct 2025
- 2025Instance-Optimal Quantum State Certification with Entangled Measurements8 Jul 2025
- 2024When can classical neural networks represent quantum states?30 Oct 2024
- 2024Learning to Classify Quantum Phases of Matter with a Few Measurements8 Sep 2024
- 2024Hybrid Classical-Quantum architecture for vectorised image classification of hand-written sketches8 Jul 2024
- 2024Learning topological states from randomized measurements using variational tensor network tomography31 May 2024