An optimal quantum algorithm to approximate the mean and its application for approximating the median of a set of points over an arbitrary distance
arXiv:1106.4267
Abstract
We describe two quantum algorithms to approximate the mean value of a black-box function. The first algorithm is novel and asymptotically optimal while the second is a variation on an earlier algorithm due to Aharonov. Both algorithms have their own strengths and caveats and may be relevant in different contexts. We then propose a new algorithm for approximating the median of a set of points over an arbitrary distance function.
Ten pages, no figures, three algorithms
Cited by in corpus (11)
- Quantum speedup of Monte Carlo methods
- Quantum computing for finance
- Low depth algorithms for quantum amplitude estimation
- Quantum Differentially Private Sparse Regression Learning
- A quantum algorithm to approximate the linear structures of Boolean functions
- Near-Optimal Quantum Algorithms for Multivariate Mean Estimation
- A Sublinear-Time Quantum Algorithm for Approximating Partition Functions
- Some Error Analysis for the Quantum Phase Estimation Algorithms
- Hybrid Quantum-Classical Algorithm For Robust Optimization via Stochastic-Gradient Online Learning
- Quantum Chebyshev's Inequality and Applications
- Quantum Coin Method for Numerical Integration