A randomized quantum algorithm for statistical phase estimation
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Abstract (arXiv)
Phase estimation is a quantum algorithm for measuring the eigenvalues of a Hamiltonian. We propose and rigorously analyse a randomized phase estimation algorithm with two distinctive features. First, our algorithm has complexity independent of the number of terms L in the Hamiltonian. Second, unlike previous L-independent approaches, such as those based on qDRIFT, all sources of error in our algorithm can be suppressed by collecting more data samples, without increasing the circuit depth.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Oct 2021.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1.
4 papers.
- 2024Quantum Boltzmann machine learning of ground-state energiescited by 12
- 2025FlowQ-Net: A Generative Framework for Automated Quantum Circuit Designcited by 4
- 2023Post-variational quantum neural networkscited by 3
- 2023Compilation of product-formula Hamiltonian simulation via reinforcement learningcited by 0
- 2025FlowQ-Net: A Generative Framework for Automated Quantum Circuit Design30 Oct 2025
- 2024Quantum Boltzmann machine learning of ground-state energies16 Oct 2024
- 2023Compilation of product-formula Hamiltonian simulation via reinforcement learning7 Nov 2023
- 2023Post-variational quantum neural networks20 Jul 2023