Discover dwave-examples/maximum-cut Open Source project

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Python
License
Apache License 2.0
Created about 5 years ago
Updated 12 months ago

dwave-examples

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Solve a max-cut problem using a quantum computer

3d-bin-packing

Use a hybrid solver to use the minimum number of bins to pack items with different dimensions

Python

job-shop-scheduling

Determine a schedule for running a set of jobs.

Python

nurse-scheduling

A demo of a nurse scheduling model

Python

ev-charger-placement

Determine where to place new charging stations on a map based on locations of existing charging stations and points of interest.

Python

portfolio-optimization

Solve different formulations of the portfolio optimization problem.

Python

knapsack

Implementation of knapsack problem, set up for scaling to large problem size

Python

factoring

Factor numbers using a quantum computer

Python

mutual-information-feature-selection

Use mutual information to select features in a Titanic data set

Python

satellite-placement

Group satellites into constellations such that their average observation coverage is maximized

Python

sudoku

Solve a Sudoku puzzle with a quantum computer

Python

structural-imbalance

Demo for analyzing the structural imbalance on a signed social network.

Python

circuit-fault-diagnosis

Find possible failing components on a circuit.

Python

clustering

Using a quantum computer to cluster data points

Python

qboost

Solve a binary classification problem with Qboost

Python

template

A template for code examples on this account. See for details on contribution guidelines.

Python

employee-scheduling

Schedule employees using a constrained quadratic model with a hybrid solver.

Python

graph-partitioning

Split a graph into two groups

Python

crop-rotation

Finds optimal crop rotations for a set of crops to be planted in a connected set of plots using the LeapHybridDQMSampler.

Python

rna-folding

Finds the optimal stem configuration of an RNA sequence using the LeapHybridCQMSampler.

Python

feature-selection-cqm

Use a hybrid solver to select features from two data sets

Python

distributed-computing

Minimize messaging between computers in a distributed computing system by using the LeapHybridCQMSampler. This problem is also known as the graph k-partitioning problem.

Python

immunization-strategy

Find the minimal number of immunization doses required to break the transmission cycle of a virus or infectious disease within a population. Solved using the LeapHybridCQMSampler.

Python

reservoir-management

Manage water levels in a reservoir by controlling water pumps.

Python

graph-coloring

A demo of graph coloring using Leap's hybrid constrained quadratic model (CQM) solver.

Python

simple-ocean-programs

Examples of introductory Ocean programs and concepts.

Python

hybrid-computing-notebook

Create and use hybrid workflows to solve problems.

Jupyter Notebook

feature-selection-notebook

Feature selection for machine learning using mutual information.

Jupyter Notebook

antenna-selection

Demonstrate a max independent set problem with antennas

Python

maze

Simple example on how to construct a problem for a quantum computer

Python

job-shop-scheduling-cqm

Determine a schedule for running a set of jobs on a certain number of machines using the LeapHybridCQMSampler.

Python

coordinated-multipoint-notebook

Use a quantum computer to decode cellphone signals

Jupyter Notebook

image-segmentation

Perform basic image segmentation using discrete quadratic models (DQM) and hybrid solvers.

Python

n-queens

Demonstrates how to formulate the n-queens problem as a QUBO, which we then solve using Leap’s hybrid solvers.

Python

cryptarithmetic

Solve cryptarithmetic addition puzzles using the LeapHybridCQMSampler.

Python

airline-hubs

Determine which airports should be hub locations for an airline. Solved using the LeapHybridCQMSampler.

Python

map-coloring

Select the colors used on the different regions of a map

Python

structural-imbalance-notebook

Analyze the structural imbalance on a signed social network.

Jupyter Notebook

factoring-notebook

Factor numbers using a quantum computer.

Jupyter Notebook

circuit-equivalence

Verify the equivalence of two electronic circuits using the LeapHybridDQMSampler.

Python

tour-planning

Use a hybrid CQM solver to optimize the modes of locomotion for a multi-leg tour

Python

paint-shop-optimization

Solve the multi-car paint shop optimization problem using the LeapHybridCQMSampler.

Python

pipelines

A minimum vertex problem with pipelines

Python

reverse-annealing-notebook

Demonstrates reverse annealing on D-Wave quantum computers.

Jupyter Notebook

frequency-selection

Solve feasibility frequency assignment problem using LeapHybridSampler.

Python

mvrp

Capacitated Vehicle Routing Problem example on D-Wave's hybrid solvers.

Python

flow-shop-scheduling

Flow Shop Scheduling example using the Quantum Hybrid NL Solver.

Python

diverse-solutions

Demonstrate techniques that help quantum applications find better, more robust solutions by comparing two generations of D-Wave 2000Q QPUs.

Python

NAE3SAT

Compare the performance of the Advantage QPU and the Advantage2 prototype on not-all-equal 3-satisfiability problems.

Python

pegasus-notebook

Learn the architecture of D-Wave's latest quantum computer.

Jupyter Notebook

kibble-zurek

Simulate Kibble-Zurek mechanism on a quantum computer

Python

dwave-examples/maximum-cut

dwave-examples

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