Quantum algorithm for estimating volumes of convex bodies
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Abstract (arXiv)
Estimating the volume of a convex body is a central problem in convex geometry and can be viewed as a continuous version of counting. We present a quantum algorithm that estimates the volume of an n-dimensional convex body within multiplicative error ε using tildeO(n3+n2.5/ε) queries to a membership oracle and tildeO(n5+n4.5/ε) additional arithmetic operations. For comparison, the best known classical algorithm uses tildeO(n4+n3/ε2) queries and tildeO(n6+n5/ε2) additional arithmetic operations. To the best of our knowledge, this is the first quantum speedup for volume estimation. Our algorithm is based on a refined framework for speeding up simulated annealing algorithms that might be of independent interest. This framework applies in the setting of "Chebyshev cooling", where the solution is expressed as a telescoping product of ratios, each having bounded variance. We develop several novel techniques when implementing our framework, including a theory of continuous-space quantum walks with rigorous bounds on discretization error. To complement our quantum algorithms, we also prove that volume estimation requires Omega(√ n+1/ε) quantum membership queries, which rules out the possibility of exponential quantum speedup in n and shows optimality of our algorithm in 1/ε up to poly-logarithmic factors.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Aug 2019.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 0; 2025: 2.
8 papers.
- 2023Beyond Classical Attention: Quantum Attention for Scalable Computationcited by 18
- 2022Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Banditscited by 11
- 2022Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constantscited by 8
- 2022A Sublinear-Time Quantum Algorithm for Approximating Partition Functionscited by 5
- 2023Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functionscited by 4
- 2021An Introduction to Quantum Computing for Statisticians and Data Scientistscited by 1
- 2025Quantum Speedups for Sampling and Non-convex Optimization with Stochastic Oraclescited by 1
- 2025On Quantum Perceptron Learning via Quantum Searchcited by 0
- 2025Quantum Speedups for Sampling and Non-convex Optimization with Stochastic Oracles4 Apr 2025
- 2025On Quantum Perceptron Learning via Quantum Search21 Mar 2025
- 2023Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions17 Oct 2023
- 2023Beyond Classical Attention: Quantum Attention for Scalable Computation16 Jul 2023
- 2022Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants12 Oct 2022
- 2022Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Bandits26 Sep 2022
- 2022A Sublinear-Time Quantum Algorithm for Approximating Partition Functions18 Jul 2022
- 2021An Introduction to Quantum Computing for Statisticians and Data Scientists13 Dec 2021