dwave-tuner 0.0.4

Tune D-Wave QPU solver parameters

D-Wave Tuner

dwave-tuner simplifies the process of tuning annealing parameters, allowing you to concentrate on your model and specific problem. Simply specify the number of embeddings, chain strengths, and annealing times, and watch as dwave-tuner tailors the tuning experience to your needs.

Features

• Effortless parameter tuning: Grid scan chain strengths and annealing times with a given number of embeddings.
• Caching for efficiency: D-Wave responses are cached by default, enhancing performance by utilizing cached results unless you modify the scan or alter the parameters.
• User-Friendly: Minimal configuration; just specify your preferences in a dictionary, and let dwave-tuner handle the details.

Example Usage

import dwavetuner

Specify the scan parameters:

parameters = {
    'num_embeddings': 10,
    'num_chain_strengths': 1,
    'num_reads': 1000,
    'num_reps': 1
}

Create a Scanner to schedule the Jobs:

scanner = dwavetuner.Scanner(my_model.bqm, label='my_model', **parameters)

Perform a grid scan

scanner.grid_scan()

The grid scan stores the results in scanner.results. It returns a response that can also be accessed via

print(scanner.response)

The verbose tuning data can be accessed via

x, ys, yerrs = scanner.scan_results('chain_strength')

For annealing time scans, replace chain_strength with annealing_time.

Note that x is one list, while ys an yerrs contain scanner.num_embeddings lists - one for each embedding.

Alternatively, use these self-explanatory parameters:

# Scan chain strengths
parameters = {
    'num_embeddings': 10,
    'chain_strengths_start': 0.24,
    'chain_strengths_end': 0.44,
    'num_chain_strengths': 10,
    'num_reads': 100,
    'num_reps': 1
}

# Scan annealing times
parameters = {
    'chain_strength': 0.28,
    'num_annealing_times': 10,
    'num_reads': 100,
    'num_reps': 5
}

Author

Orkun Şensebat

[e-mail]