Schedule employees using a constrained quadratic model with a hybrid solver.
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Updated
May 23, 2024 - Python
Schedule employees using a constrained quadratic model with a hybrid solver.
A demo of a nurse scheduling model
Determine a schedule for running a set of jobs on a certain number of machines using the LeapHybridCQMSampler.
Use a hybrid solver to use the minimum number of bins to pack items with different dimensions
Use a hybrid CQM solver to optimize the modes of locomotion for a multi-leg tour
A demo of graph coloring using Leap's hybrid constrained quadratic model (CQM) solver.
Use a hybrid solver to select features from two data sets
Solve a Sudoku puzzle with a quantum computer
Manage water levels in a reservoir by controlling water pumps.
Group satellites into constellations such that their average observation coverage is maximized
Finds the optimal stem configuration of an RNA sequence using the LeapHybridCQMSampler.
Solve different formulations of the portfolio optimization problem.
Solve the multi-car paint shop optimization problem using the LeapHybridCQMSampler.
Demonstrates how to formulate the n-queens problem as a QUBO, which we then solve using Leap’s hybrid solvers.
Select the colors used on the different regions of a map
Implementation of knapsack problem, set up for scaling to large problem size
Optimize the initial line-up of Liverpool FC using the LeapHybridSampler.
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.
Perform basic image segmentation using discrete quadratic models (DQM) and hybrid solvers.
Create and use hybrid workflows to solve problems.
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