mlsanityData to 7 Oct 2026

Efficient estimation of Pauli observables by derandomization

arXiv 2103.07510 ↗First posted 12 Mar 2021quant-ph cs.DScited by 20

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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.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 5; 2024: 5; 2025: 4; 2026: 1.

20 papers.

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  1. 2021Exponential separations between learning with and without quantum memorycited by 50
  2. 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerancecited by 32
  3. 2023A Unified Framework for Trace-induced Quantum Kernelscited by 12
  4. 2025Generative quantum advantage for classical and quantum problemscited by 9
  5. 2022Predicting Properties of Quantum Systems with Conditional Generative Modelscited by 8
  6. 2021Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuitscited by 7
  7. 2023Exponentially improved efficient machine learning for quantum many-body states with provable guaranteescited by 7
  8. 2023ShadowNet for Data-Centric Quantum System Learningcited by 7
  9. 2024Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadowscited by 7
  10. 2024Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuitscited by 7
  11. 2022Hardware-efficient learning of quantum many-body statescited by 5
  12. 2023Learning Quantum Processes with Quantum Statistical Queriescited by 4
8 more
  1. 2025Rethink the Role of Deep Learning towards Large-scale Quantum Systemscited by 4
  2. 2023Post-variational quantum neural networkscited by 3
  3. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  4. 2021Towards a variational Jordan-Lee-Preskill quantum algorithmcited by 1
  5. 2025Online Learning of Pure States is as Hard as Mixed Statescited by 1
  6. 2024Predicting adaptively chosen observables in quantum systemscited by 0
  7. 2024Classical Shadows with Improved Median-of-Means Estimationcited by 0
  8. 2026Generative Learning for Quantum Measurement Designcited by 0