Quantum Amplitude Estimation for Probabilistic Methods in Power Systems

TL;DR

This paper explores quantum computing techniques, specifically Quantum Amplitude Estimation methods, to significantly accelerate Monte Carlo simulations in power systems, reducing computational effort while maintaining accuracy.

Contribution

It introduces and compares three Quantum Amplitude Estimation methods tailored for power system Monte Carlo simulations, demonstrating potential quantum speedups.

Findings

Quantum methods require fewer samples for accurate estimates.

IQAE, MLAE, and FAE outperform classical Monte Carlo in speed.

Quantum approaches show promise for large-scale power system analysis.

Abstract

This paper introduces quantum computing methods for Monte Carlo simulations in power systems which are expected to be exponentially faster than their classical computing counterparts. Monte Carlo simulations is a fundamental method, widely used in power systems to estimate key parameters of unknown probability distributions, such as the mean value, the standard deviation, or the value at risk. It is, however, very computationally intensive. Approaches based on Quantum Amplitude Estimation can offer a quadratic speedup, requiring orders of magnitude less samples to achieve the same accuracy. This paper explains three Quantum Amplitude Estimation methods to replace the Classical Monte Carlo method, namely the Iterative Quantum Amplitude Estimation (IQAE), Maximum Likelihood Amplitude Estimation (MLAE), and Faster Amplitude Estimation (FAE), and compares their performance for three different types of probability distributions for power systems.