sampleset.md

The table seems to represent solutions sampled from a D-Wave quantum computer for an optimization problem. Each row represents a different sampled solution, with the variables taking values in the columns 1-5. The energy column indicates the objective function value for each solution, with a more negative value being better. The num_oc. column may represent the number of occurrences of each sample, and chain_. could signify chain breaks if you are using chain strength in your quantum annealing algorithm.

Mathematical Representation:

Let ( x_1, x_2, x_3, x_4, x_5 ) be the variable assignments for the five columns (1 to 5).

The energy can often be represented in the form of an objective function, say ( E(x_1, x_2, x_3, x_4, x_5) ), that the quantum annealing process aims to minimize.

For example, in the Ising model, the energy function ( E ) might look like:

[ E(x_1, x_2, x_3, x_4, x_5) = - \sum_{i<j} J_{ij} x_i x_j - \sum_i h_i x_i ]

Where ( J_{ij} ) and ( h_i ) are constants. In the case of DQM, you might have a more generalized form to include discrete variables.

Python3 Code to Parse the Table:

Let's assume that you get the table from D-Wave's sampler as a Python dictionary; you can parse it like this:

# Example data received from D-Wave sampler
sample_data = [
    {'sample': {0: 1, 1: 0, 2: 0, 3: 1, 4: 1}, 'energy': -9.0, 'num_oc.': 4, 'chain_.': 0.0},
    {'sample': {0: 1, 1: 0, 2: 1, 3: 1, 4: 0}, 'energy': -8.0, 'num_oc.': 6, 'chain_.': 0.2},
    # ... additional samples
]

# Parsing data
for idx, data in enumerate(sample_data):
    sample = data['sample']
    energy = data['energy']
    num_oc = data['num_oc.']
    chain_ = data['chain_.']
    
    print(f"Sample {idx + 1}: {sample}, Energy: {energy}, Occurrences: {num_oc}, Chain Breaks: {chain_}")

This will give you a more manipulable form of the data in Python, making it easier to analyze and draw insights.