Classical Shadows With Noise
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Abstract (arXiv)
The classical shadows protocol, recently introduced by Huang, Kueng, and Preskill [Nat. Phys. 16, 1050 (2020)], is a quantum-classical protocol to estimate properties of an unknown quantum state. Unlike full quantum state tomography, the protocol can be implemented on near-term quantum hardware and requires few quantum measurements to make many predictions with a high success probability. In this paper, we study the effects of noise on the classical shadows protocol. In particular, we consider the scenario in which the quantum circuits involved in the protocol are subject to various known noise channels and derive an analytical upper bound for the sample complexity in terms of a shadow seminorm for both local and global noise. Additionally, by modifying the classical post-processing step of the noiseless protocol, we define a new estimator that remains unbiased in the presence of noise. As applications, we show that our results can be used to prove rigorous sample complexity upper bounds in the cases of depolarizing noise and amplitude damping.
Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Nov 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 5; 2025: 2; 2026: 3.
14 papers.
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- 2022Learning to predict arbitrary quantum processescited by 35
- 2025A Primer on Quantum Machine Learningcited by 12
- 2024Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadowscited by 7
- 2022Hardware-efficient learning of quantum many-body statescited by 5
- 2021Hamiltonian-Driven Shadow Tomography of Quantum Statescited by 4
- 2025Scalable bayesian shadow tomography for quantum property estimation with set transformerscited by 1
- 2026Quantum-Inspired Self-Attention in a Large Language Modelcited by 1
- 2023A quantum tug of war between randomness and symmetries on homogeneous spacescited by 0
- 2024Noise-tolerant learnability of shallow quantum circuits from statistics and the cost of quantum pseudorandomnesscited by 0
- 2024Principal eigenstate classical shadowscited by 0
- 2024Predicting adaptively chosen observables in quantum systemscited by 0
- 2024Classical Shadows with Improved Median-of-Means Estimationcited by 0
- 2026Symmetry Discovery in Quantum Learning: Observable-Level and Task-Level Inference from Finite Measurements11 Sep 2026
- 2026Privacy-Utility Tradeoffs in Quantum Information Processing11 Feb 2026
- 2026Quantum-Inspired Self-Attention in a Large Language Model9 Feb 2026
- 2025A Primer on Quantum Machine Learning20 Nov 2025
- 2025Scalable bayesian shadow tomography for quantum property estimation with set transformers23 Sep 2025
- 2024Classical Shadows with Improved Median-of-Means Estimation4 Dec 2024
- 2024Predicting adaptively chosen observables in quantum systems20 Oct 2024
- 2024Principal eigenstate classical shadows22 May 2024
- 2024Noise-tolerant learnability of shallow quantum circuits from statistics and the cost of quantum pseudorandomness20 May 2024
- 2024Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows27 Feb 2024
- 2023A quantum tug of war between randomness and symmetries on homogeneous spaces11 Sep 2023
- 2022Hardware-efficient learning of quantum many-body states12 Dec 2022
2 more
- 2022Learning to predict arbitrary quantum processes26 Oct 2022
- 2021Hamiltonian-Driven Shadow Tomography of Quantum States19 Feb 2021
- 2022Learning to predict arbitrary quantum processescited by 35
- 2025A Primer on Quantum Machine Learningcited by 12
- 2024Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadowscited by 7
- 2022Hardware-efficient learning of quantum many-body statescited by 5
- 2021Hamiltonian-Driven Shadow Tomography of Quantum Statescited by 4
- 2025Scalable bayesian shadow tomography for quantum property estimation with set transformerscited by 1
- 2026Quantum-Inspired Self-Attention in a Large Language Modelcited by 1
- 2023A quantum tug of war between randomness and symmetries on homogeneous spacescited by 0
- 2024Noise-tolerant learnability of shallow quantum circuits from statistics and the cost of quantum pseudorandomnesscited by 0
- 2024Principal eigenstate classical shadowscited by 0
- 2024Predicting adaptively chosen observables in quantum systemscited by 0
- 2024Classical Shadows with Improved Median-of-Means Estimationcited by 0
12 most cited of 14 All 14 (JSON)
- 2026Symmetry Discovery in Quantum Learning: Observable-Level and Task-Level Inference from Finite Measurements11 Sep 2026
- 2026Privacy-Utility Tradeoffs in Quantum Information Processing11 Feb 2026
- 2026Quantum-Inspired Self-Attention in a Large Language Model9 Feb 2026
- 2025A Primer on Quantum Machine Learning20 Nov 2025
- 2025Scalable bayesian shadow tomography for quantum property estimation with set transformers23 Sep 2025
- 2024Classical Shadows with Improved Median-of-Means Estimation4 Dec 2024
- 2024Predicting adaptively chosen observables in quantum systems20 Oct 2024
- 2024Principal eigenstate classical shadows22 May 2024
- 2024Noise-tolerant learnability of shallow quantum circuits from statistics and the cost of quantum pseudorandomness20 May 2024
- 2024Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows27 Feb 2024
- 2023A quantum tug of war between randomness and symmetries on homogeneous spaces11 Sep 2023
- 2022Hardware-efficient learning of quantum many-body states12 Dec 2022
12 newest of 14 All 14 (JSON)