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Version: 2.0

BQPhy Python SDK

This documentation provides the steps to install, configure, and execute optimization jobs using the BQPhy Python SDK.

1. Installation​

Install the BQPhy Client using the provided wheel file via pip. Ensure the .whl file is in your current directory.

pip install bqphy_client-0.1.1-py3-none-any.whl

2. Configuration​

The SDK requires two configuration files located in the .bqphy directory within your user home folder (e.g., ~/.bqphy/ or C:\Users\Username\.bqphy\).

secrets.json​

Add your authentication token here:

{
    "TOKEN": "YOUR_API_TOKEN_HERE"
}

config.json​

Add your environment-specific details (API URL, Tenant ID, etc.):

{
  "API_URL": "https://bqphy.bqpsim.com",
  "TENANT_ID": "your_account_id",
  "SERVER_ID": "server_id",
  "REALM": "bqphy",
  "skip_upload_on_error": false,
  "optimization": {
    "trials": 1,
    "poll_interval": 30,
    "max_wait": 3600
  }
}

3. Prepare the Optimization Model​

Create a Python file (e.g., FO_Knapsack_500.py) containing your optimization logic. This file must define the function or class (e.g., Evaluator) that the BQPhy engine will invoke.

4. Submitting a Job​

Use the following Python snippet to initialize the optimizer and submit your job to the BQPhy API.

from bqphy_client.optimiser import BQPhy_API

# 1. Define Optimization Parameters
OPTIMIZATION_PARAMS = {
    "designVariables": 500,
    "numPopulation": 50,
    "maxGeneration": 100,
    "deltaTheta": 0.1,
    "typeOfOptimisation": "Discrete"
}

# 2. Initialize the API Client
optimizer = BQPhy_API()

# 3. Configure Model Details
optimizer.model_file_path("FO_Knapsack_500.py")
optimizer.model("Evaluator")

# 4. Initialize credentials from home directory and run
optimizer.initialize_from_home(OPTIMIZATION_PARAMS)
optimizer.run(log_level="info")

5. Output and Results​

Once the execution is complete, the SDK downloads the output data automatically.

  • Location: Results are generated under the results directory in your current working directory (./results).
  • Logs: You can monitor the progress in the terminal via the log_level="info" parameter.