Efficient estimation of Pauli observables by derandomization
Not on the map. Why?
Abstract (arXiv)
We consider the problem of jointly estimating expectation values of many Pauli observables, a crucial subroutine in variational quantum algorithms. Starting with randomized measurements, we propose an efficient derandomization procedure that iteratively replaces random single-qubit measurements with fixed Pauli measurements; the resulting deterministic measurement procedure is guaranteed to perform at least as well as the randomized one. In particular, for estimating any L low-weight Pauli observables, a deterministic measurement on only of order log(L) copies of a quantum state suffices. In some cases, for example when some of the Pauli observables have a high weight, the derandomized procedure is substantially better than the randomized one. Specifically, numerical experiments highlight the advantages of our derandomized protocol over various previous methods for estimating the ground-state energies of small molecules.
Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 2; 2023: 5; 2024: 5; 2025: 4; 2026: 1.
20 papers.
Sort:
- 2021Exponential separations between learning with and without quantum memorycited by 50
- 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerancecited by 32
- 2023A Unified Framework for Trace-induced Quantum Kernelscited by 12
- 2025Generative quantum advantage for classical and quantum problemscited by 9
- 2022Predicting Properties of Quantum Systems with Conditional Generative Modelscited by 8
- 2021Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuitscited by 7
- 2023Exponentially improved efficient machine learning for quantum many-body states with provable guaranteescited by 7
- 2023ShadowNet for Data-Centric Quantum System Learningcited by 7
- 2024Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadowscited by 7
- 2024Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuitscited by 7
- 2022Hardware-efficient learning of quantum many-body statescited by 5
- 2023Learning Quantum Processes with Quantum Statistical Queriescited by 4
8 more
- 2025Rethink the Role of Deep Learning towards Large-scale Quantum Systemscited by 4
- 2023Post-variational quantum neural networkscited by 3
- 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
- 2021Towards a variational Jordan-Lee-Preskill quantum algorithmcited by 1
- 2025Online Learning of Pure States is as Hard as Mixed Statescited by 1
- 2024Predicting adaptively chosen observables in quantum systemscited by 0
- 2024Classical Shadows with Improved Median-of-Means Estimationcited by 0
- 2026Generative Learning for Quantum Measurement Designcited by 0
- 2026Generative Learning for Quantum Measurement Design11 Aug 2026
- 2025Generative quantum advantage for classical and quantum problems10 Sep 2025
- 2025Adversarial Robustness in Distributed Quantum Machine Learning16 Aug 2025
- 2025Rethink the Role of Deep Learning towards Large-scale Quantum Systems20 May 2025
- 2025Online Learning of Pure States is as Hard as Mixed States2 Feb 2025
- 2024Classical Shadows with Improved Median-of-Means Estimation4 Dec 2024
- 2024Predicting adaptively chosen observables in quantum systems20 Oct 2024
- 2024Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits22 Aug 2024
- 2024Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows27 Feb 2024
- 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance21 Jan 2024
- 2023A Unified Framework for Trace-induced Quantum Kernels22 Nov 2023
- 2023Learning Quantum Processes with Quantum Statistical Queries3 Oct 2023
8 more
- 2023ShadowNet for Data-Centric Quantum System Learning22 Aug 2023
- 2023Post-variational quantum neural networks20 Jul 2023
- 2023Exponentially improved efficient machine learning for quantum many-body states with provable guarantees10 Apr 2023
- 2022Hardware-efficient learning of quantum many-body states12 Dec 2022
- 2022Predicting Properties of Quantum Systems with Conditional Generative Models30 Nov 2022
- 2021Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuits15 Nov 2021
- 2021Exponential separations between learning with and without quantum memory10 Nov 2021
- 2021Towards a variational Jordan-Lee-Preskill quantum algorithm12 Sep 2021
- 2021Exponential separations between learning with and without quantum memorycited by 50
- 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerancecited by 32
- 2023A Unified Framework for Trace-induced Quantum Kernelscited by 12
- 2025Generative quantum advantage for classical and quantum problemscited by 9
- 2022Predicting Properties of Quantum Systems with Conditional Generative Modelscited by 8
- 2021Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuitscited by 7
- 2023Exponentially improved efficient machine learning for quantum many-body states with provable guaranteescited by 7
- 2023ShadowNet for Data-Centric Quantum System Learningcited by 7
- 2024Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadowscited by 7
- 2024Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuitscited by 7
- 2022Hardware-efficient learning of quantum many-body statescited by 5
- 2023Learning Quantum Processes with Quantum Statistical Queriescited by 4
12 most cited of 20 All 20 (JSON)
- 2026Generative Learning for Quantum Measurement Design11 Aug 2026
- 2025Generative quantum advantage for classical and quantum problems10 Sep 2025
- 2025Adversarial Robustness in Distributed Quantum Machine Learning16 Aug 2025
- 2025Rethink the Role of Deep Learning towards Large-scale Quantum Systems20 May 2025
- 2025Online Learning of Pure States is as Hard as Mixed States2 Feb 2025
- 2024Classical Shadows with Improved Median-of-Means Estimation4 Dec 2024
- 2024Predicting adaptively chosen observables in quantum systems20 Oct 2024
- 2024Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits22 Aug 2024
- 2024Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows27 Feb 2024
- 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance21 Jan 2024
- 2023A Unified Framework for Trace-induced Quantum Kernels22 Nov 2023
- 2023Learning Quantum Processes with Quantum Statistical Queries3 Oct 2023
12 newest of 20 All 20 (JSON)