Quadratic Unconstrained Binary Optimization (QUBO)
Introduction
The Quadratic Unconstrained Binary Optimization (QUBO) is a foundational problem type used in many fields to formulate discrete combinatorial optimization problems. Generically, a Qubo is defined by linear and quadratic terms, but since when , we can simplify the optimization expression as such , where . Note that the coefficients naturally encodes as a square symmetric matrix, so that , where has entries . The goal of the optimization problem is to find the binary vector, , that minimizes ,
.
Running a QUBO job
Data format
To upload a square symmetric matrix or Qubo, we encode it in a sparse matrix format as shown below. We use Numpy array notation.
1
Q = np.array([[0, -1.5, 0.5], [-1.5, 0, 0], [0.5, 0, 0]]) ,
then the JSON will take the following list of dictionary sparse matrix format:
{(i,j): value}
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qubo_data = {
"data": [
{
"i": 0,
"j": 1,
"val": -1.5
},
{
"i": 0,
"j": 2,
"val": 0.5
},
{
"i": 1,
"j": 0,
"val": -1.5
},
{
"i": 2,
"j": 0,
"val": 0.5
}
],
"file_name": "smallest_objective.json", # can be any short string
"num_variables": 3, # number of rows
"file_type": "qubo" # defines the data type, 'qubo' in this case
}
Uploading
To upload the matrix encoded above in `qubo_data`, we use the the `qci_client` imported previously. The following line
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response_json = qci.upload_file(qubo_data)
The response contains a
file_id
for the uploaded file. This id is provided when a job is run, along a few other parameters (see #Running). Note: the same `file_id` can be used multiple times to run a problem repeatedly. This enables an "upload once, run many times" scheme, which is especially useful for job types in which parameter searches may be involved.