Quantum speedup of non-linear Monte Carlo problems

TL;DR

This paper introduces a quantum Monte Carlo algorithm that achieves a quadratic speedup for estimating non-linear functionals of probability distributions, extending quantum advantages beyond linear mean estimation.

Contribution

The paper presents a novel quantum-inside-quantum Monte Carlo algorithm for non-linear functionals, improving upon previous methods and demonstrating near-optimal performance with a new multilevel approximation technique.

Findings

Achieves quadratic speedup for non-linear functional estimation

Improves upon previous quantum Monte Carlo algorithms

Algorithm is near-optimal up to polylogarithmic factors

Abstract

The mean of a random variable can be understood as a linear functional on the space of probability distributions. Quantum computing is known to provide a quadratic speedup over classical Monte Carlo methods for mean estimation. In this paper, we investigate whether a similar quadratic speedup is achievable for estimating non-linear functionals of probability distributions. We propose a quantum-inside-quantum Monte Carlo algorithm that achieves such a speedup for a broad class of non-linear estimation problems, including nested conditional expectations and stochastic optimization. Our algorithm improves upon the direct application of the quantum multilevel Monte Carlo algorithm introduced by An et al. (2021). The existing lower bound indicates that our algorithm is optimal up polylogarithmic factors. A key innovation of our approach is a new sequence of multilevel Monte Carlo approximations specifically designed for quantum computing, which is central to the algorithm's improved performance.