Welcome to Quixotic

What is Quixotic?

Quixotic is a Python library for low-code, simple-to-use quantum computing.

Features

• Easy-to-apply quantum algorithms to a number of combinatorial optimization problems using Quantum Annealing and QAOA.
• Includes out-of-the-box support for various optimization problems like maximum clique and minimum vertex cover.
• Supports execution using both local simulation on your laptop and managed quantum computers on Amazon Braket.

Install

1. pip install -U pip
2. pip install quixotic

NOTE: Python version >= 3.7 is required.

Usage: Find Maximum Clique in a Graph

# construct or load your input graph
import networkx as nx
n_nodes = 6
p = 0.5  # probability of an edge
seed = 1967
g = nx.erdos_renyi_graph(n_nodes, p=p, seed=seed)
positions = nx.spring_layout(g, seed=seed)
nx.draw(g, with_labels=True, pos=positions, node_size=600)

# approximate a solution using QuantumAnnealer and extract results
from quixotic.core import QuantumAnnealer
qo = QuantumAnnealer(g, task='maximum_clique').fit()
nodes = qo.results()

# plot nodes comprising the solution
sub = g.subgraph(nodes)
nx.draw(g, pos=positions, with_labels=True)
nx.draw(sub, pos=positions, node_color="r", edge_color="r")

To execute on a Quantum Computer

By default, Quixotic uses a local solver or simulator (e.g., simulated annealing insetad of quantum annealing), which allows you to easily run and test on your CPU-based laptop. To run on an actual managed quantum computer hosted on Amazon Braket, simply set the device_arn and s3_folder parameters. For QuantumAnnealer, the device_arn should be a D-Wave QPU:

# running on Amazon Braket
from quixotic.core import QuantumAnnealer
qo = QuantumAnnealer(g, task='maximum_clique',
                    device_arn='arn:aws:braket:::device/qpu/d-wave/DW_2000Q_6',  # D-Wave QPU
                    s3_folder = ("amazon-braket-Your-Bucket-Name", "Your-Folder-Name"))
qo.fit()
nodes = qo.results()