Quantum speedup of non-linear Monte Carlo problems
This work addresses a fundamental limitation in quantum computing for non-linear Monte Carlo problems, offering a significant speedup over classical methods, though it builds incrementally on prior quantum multilevel Monte Carlo techniques.
The paper tackles the problem of estimating non-linear functionals of probability distributions, achieving a quadratic quantum speedup for a broad class of problems, including nested conditional expectations and stochastic optimization, with the algorithm being optimal up to polylogarithmic factors.
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.