Quantum-Simulation-Optimisation

This repo accompanies the GECCO 2019 paper "Optimising Trotter-Suzuki Decompositions for Quantum Simulation Using Evolutionary Strategies" by Benjamin D.M. Jones, David R. White, George O. O'Brien, John A. Clark, Earl T. Campbell.

The code was written by Ben and David.

Preprint of this paper via arxiv.

The code optimises the approximation of a Heisenberg Chain using Trotter-Suzuki decomposition.

The core source code is in the qsim directory, with tests in test.

Experiments are in experiments, with a readme containing summaries of each experiment.

Parameter files are split across results and experiments.

Results files are in csv format and are in results. Often these files had to be recombined and sorted to be ran on a HPC cluster.

Example

To create an instance of a HeisenbergChain:

import numpy as np
import hchain

n = 5 # number of qubits/system size
v = np.random.uniform(n)

chain = hchain.HeisenbergChain(n=n,v=v)

Then to get the error of a particular Trotter-Suzuki Decomposition (specified by k and r):

import approx

k = 2
r = 100
lambda_vector = approx.suzuki_solution(k,r)

t = 2 * chain.n  # we simulate for time proportional to system size

error = approx.error(chain=chain, lambda_v=lambda_vector, t=t)