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With GCP

info

For detailed instructions on setting up your GCP bucket, refer to the Bucket Management documentation.

We can now start issuing problems to the TitanQ Solver. Below are some examples with expected results that can be run from your favorite IDE and Python environments locally.

Note that the following Python modules need to be installed:

  • google-cloud-storage (for Google Cloud Bucket access)
  • numpy (for data generation exchange)

Generate a weights matrix and a bias vector​

Here are some example input weights matrix and bias vectors:

import numpy as np
weights = np.array([[ 0, -1, -1, -1,  1, -1,  1, -1, -1,  1],
                    [-1,  0,  1, -1, -1,  1, -1, -1, -1,  1],
                    [-1,  1,  0, -1,  1, -1,  1, -1, -1, -1],
                    [-1, -1, -1,  0, -1,  1,  1, -1,  1, -1],
                    [ 1, -1,  1, -1,  0, -1, -1,  1, -1,  1],
                    [-1,  1, -1,  1, -1,  0, -1, -1,  1,  1],
                    [ 1, -1,  1,  1, -1, -1,  0,  1,  1,  1],
                    [-1, -1, -1, -1,  1, -1,  1,  0,  1,  1],
                    [-1, -1, -1,  1, -1,  1,  1,  1,  0,  1],
                    [ 1,  1, -1, -1,  1,  1,  1,  1,  1,  0]], dtype=np.float32)
bias = np.zeros(len(weights), dtype=np.float32)
np.save('weights.npy', weights)
np.save('bias.npy', bias)

Upload the two files to the source bucket​

From the Google Cloud Bucket dashboard, navigate to your source bucket, then select UPLOAD FILES:

upload_bucket.png

or programmatically

from google.cloud import storage
from google.oauth2 import service_account

creds = service_account.Credentials.from_service_account_info(
    # TODO insert your WRITE key here from the downloaded .json file
)
storage_client = storage.Client(credentials=creds)
bucket = storage_client.bucket("source-bucket-uniquename")

blob = bucket.blob("weights-file-path")
blob.upload_from_filename("YOUR_LOCAL_WEIGHTS_FILE.npy")

blob = bucket.blob("bias-file-path")
blob.upload_from_filename("YOUR_LOCAL_BIAS_FILE.npy")

Issuing a solve request​

Submitting a request to the TitanQ API involves preparing a JSON body and some HTTP Header parameters.

note

In the example below

  • make sure to fill in...

    • your API key (API_KEY)
    • your source bucket name (SOURCE_BUCKET_NAME)
    • your destination bucket name (DEST_BUCKET_NAME)
    • your project name (PROJECT_NAME)

  • make sure to have these files

note

These problems were originally meant as bipolar problems. Here, we solve them as binary. To use them in the original bipolar-native setting, please use the bipolar-to-binary converter provided in the TitanQ SDK.

import io
import json
import numpy as np
import requests
import time
import zipfile
from google.cloud import storage
from google.oauth2 import service_account


def main():
  hdr = {
    'content-type': 'application/json',
    'authorization': 'API_KEY'
  }

  titanq_req_body = {
    'input': {
      'bias_file_name': 'bias.npy',
      'weights_file_name': 'weights.npy',
      'gcp': {
        'bucket_name': 'SOURCE_BUCKET_NAME',
        'json_key': {} # TODO insert your READ key here from the downloaded .json file
      }
    },
    'output': {
      'gcp': {
        'bucket_name': 'DEST_BUCKET_NAME',
        'json_key': {} # TODO insert your WRITE key here from the downloaded .json file
      },
      'result_archive_file_name': 'titanq/quickstart_result.zip'
    },
    'parameters': {
      'beta': (1/(np.linspace(0.5, 100, 8, dtype=np.float32))).tolist(),
      'coupling_mult': 0.5,
      'num_chains': 8,
      'num_engines': 4,
      'timeout_in_secs': 0.1,
      'variable_types': 'b'*10,
    }
  }

  try:
    resp = requests.post(
      'https://titanq.infinityq.io/v1/solve',
      headers=hdr,
      data=json.dumps(titanq_req_body))

    if 200 <= resp.status_code < 300:
      # Parse the request's confirmation
      titanq_response_body = json.loads(resp.content)
      message = titanq_response_body['message']
      status = titanq_response_body['status']
      computation_id = titanq_response_body['computation_id']

      # Wait for computation to complete.
      # Guesstimate, assuming no delay in queue, etc.
      time.sleep(titanq_req_body['parameters']['timeout_in_secs'] + 3)

      creds = service_account.Credentials.from_service_account_info(
        # TODO insert your READ key here from the downloaded .json file
      )
      storage_client = storage.Client(credentials=creds)
      bucket = storage_client.bucket(titanq_req_body['output']['gcp']['bucket_name'])
      blob = bucket.blob(titanq_req_body['output']['result_archive_file_name'])
      buff = io.BytesIO(blob.download_as_bytes())
      
      # unzip to the current directory
      with zipfile.ZipFile(buff, 'r') as zip_file:
        zip_file.extractall(".")

      # Inspect results
      with open('metrics.json', 'r') as metrics:
        metrics = json.load(metrics)

        print("-" * 15, "+", "-" * 22, sep="")
        print("Ising energy   | Expected ising energy")
        print("-" * 15, "+", "-" * 22, sep="")
        for ising_energy in metrics['ising_energy']:
            print(f"{ising_energy: <14f} | -35")

    else:
      print('Request yielded HTTP {}'.format(resp.status))
  except Exception as e:
    print('Exception ', e)


if __name__ == '__main__':
  main()