JuliQAOA

A fast, flexible package for simulating the Quantum Alternating Operator Ansatz (QAOA).

Documentation

Please see our full documentation here.

Installation

The latest stable release of JuliQAOA can be installed using the Julia package manager with

julia> import Pkg
julia> Pkg.add(url="https://github.com/lanl/JuliQAOA.jl")

or

julia> # hit the `]` button to enter the package manager
(@v1.9) pkg> add https://github.com/lanl/JuliQAOA.jl

Usage

The core functionality of JuliQAOA is to take in a set of angles ${\beta_i, \gamma_i}$, a mixer $H_M$, and a cost function $H_C$, and return the statevector

$$|\psi(\beta, \gamma)\rangle = e^{-i \beta_p H_M} e^{-i \gamma_p H_C} \dots e^{-i \beta_1 H_M} e^{-i \gamma_1 H_C} |\psi_0\rangle.$$

Here is a simple example for a 6-qubit MaxCut problem:

using JuliQAOA, Graphs

n = 6

# 3 rounds with random angles
p = 3
# angles[1:p] = betas, angles[p+1:end] = gammas
angles = rand(2*p)

# transverse field mixer
mixer = mixer_x(n) 

# calculate the MaxCut cost function over all basis states on a random G(n,p) graph
g = erdos_renyi(n, 0.5)
obj_vals = [maxcut(g, x) for x in states(n)]

# calculate the statevector (with |ψ0⟩ = uniform superposition over all states)
statevector(angles, mixer, obj_vals)

The statevector can then be used to calculate other quantities of interest, e.g. the expectation value of H_C or ground state probability.

Contributing

Please report any issues, bugs, feature requests, suggestions for improvement, etc., via the Github issue tracker.

The primary developer of this package is John Golden (email, github).

License

This software is provided under a BSD license with a "modifications must be indicated" clause. See the LICENSE file for the full text.

LANL C Number: C22038

Alternatives

QAOA can be simulated in general-purpose quantum simulators, e.g. Qiskit and Pennylane, however they will be significantly slower.

QAOA.jl is a circuit-based QAOA simulator for Julia. QOKit is a Python package which uses many of the same basic ideas as JuliQAOA, in particular precomputation and caching of the cost function terms. It is currently more geared towards running highly parallelized simulations on large computer clusters.

Citation

If you find JuliQAOA helpful in your work, please cite

@inproceedings{10.1145/3624062.3624220, 
    author = {Golden, John and Baertschi, Andreas and O'Malley, Dan and Pelofske, Elijah and Eidenbenz, Stephan}, 
    title = {JuliQAOA: Fast, Flexible QAOA Simulation},
    year = {2023}, 
    isbn = {9798400707858},
    publisher = {Association for Computing Machinery}, 
    address = {New York, NY, USA}, 
    url = {https://doi.org/10.1145/3624062.3624220}, 
    doi = {10.1145/3624062.3624220},
    booktitle = {Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis}, 
    pages = {1454–1459}, 
    numpages = {6}, 
    location = {Denver, CO, USA}, 
    series = {SC-W '23} }