# Optimising Trotter-Suzuki Decompositions for Quantum Simulation Using Evolutionary Strategies

## TL;DR

This paper applies an evolutionary optimization algorithm to improve Trotter-Suzuki decompositions for quantum simulation, significantly reducing errors in simulating quantum systems like the Heisenberg Chain.

## Contribution

It introduces an evolutionary strategy to optimize Trotter-Suzuki decompositions, achieving substantial error reduction and demonstrating robustness and generalization across system sizes.

## Key findings

- Reduced simulation error by around 60%
- Optimization results are robust across multiple runs
- Generalizes to larger quantum systems

## Abstract

One of the most promising applications of near-term quantum computing is the simulation of quantum systems, a classically intractable task. Quantum simulation requires computationally expensive matrix exponentiation; Trotter-Suzuki decomposition of this exponentiation enables efficient simulation to a desired accuracy on a quantum computer. We apply the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) algorithm to optimise the Trotter-Suzuki decompositions of a canonical quantum system, the Heisenberg Chain; we reduce simulation error by around 60%. We introduce this problem to the computational search community, show that an evolutionary optimisation approach is robust across runs and problem instances, and find that optimisation results generalise to the simulation of larger systems.