# A randomized quantum algorithm for statistical phase estimation

arXiv: https://arxiv.org/abs/2110.12071 · HTML: https://mlsanity.com/paper/2110.12071/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2021.

## Built on by (most cited first, 4 of 4)

- [Quantum Boltzmann machine learning of ground-state energies](https://mlsanity.com/paper/2410.12935/) (2024)
- [FlowQ-Net: A Generative Framework for Automated Quantum Circuit Design](https://mlsanity.com/paper/2510.26688/) (2025)
- [Post-variational quantum neural networks](https://mlsanity.com/paper/2307.10560/) (2023)
- [Compilation of product-formula Hamiltonian simulation via reinforcement learning](https://mlsanity.com/paper/2311.04285/) (2023)

## 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.