Quantum Approximate Counting, Simplified
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Abstract (arXiv)
In 1998, Brassard, Hoyer, Mosca, and Tapp (BHMT) gave a quantum algorithm for approximate counting. Given a list of N items, K of them marked, their algorithm estimates K to within relative error varepsilon by making only Oleft( frac1varepsilon√fracNKright) queries. Although this speedup is of "Grover" type, the BHMT algorithm has the curious feature of relying on the Quantum Fourier Transform (QFT), more commonly associated with Shor's algorithm. Is this necessary? This paper presents a simplified algorithm, which we prove achieves the same query complexity using Grover iterations only. We also generalize this to a QFT-free algorithm for amplitude estimation. Related approaches to approximate counting were sketched previously by Grover, Abrams and Williams, Suzuki et al., and Wie (the latter two as we were writing this paper), but in all cases without rigorous analysis.
Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). First posted on arXiv 28 Aug 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
6 papers.
- 2023Des-q: a quantum algorithm to provably speedup retraining of decision treescited by 4
- 2024Discrete Randomized Smoothing Meets Quantum Computingcited by 2
- 2021Quantum Boosting using Domain-Partitioning Hypothesescited by 1
- 2022Tunable Quantum Neural Networks in the QPAC-Learning Frameworkcited by 0
- 2023Efficient option pricing with unary-based photonic computing chip and generative adversarial learningcited by 0
- 2026Towards Noise-Resilient Quantum Multi-Armed and Stochastic Linear Banditscited by 0
- 2026Towards Noise-Resilient Quantum Multi-Armed and Stochastic Linear Bandits19 Mar 2026
- 2024Discrete Randomized Smoothing Meets Quantum Computing1 Aug 2024
- 2023Des-q: a quantum algorithm to provably speedup retraining of decision trees18 Sep 2023
- 2023Efficient option pricing with unary-based photonic computing chip and generative adversarial learning8 Aug 2023
- 2022Tunable Quantum Neural Networks in the QPAC-Learning Framework3 May 2022
- 2021Quantum Boosting using Domain-Partitioning Hypotheses25 Oct 2021