IonQ API Service

IonQ's API provides a way to execute quantum circuits on IonQ's trapped ion quantum computers or on cloud based simulators. As of April 2021 this access is restricted to partners. See Access and Authentication for details of access.

Service class

The main entrance for accessing IonQ's API are instances of the cirq.ionq.Service class. These objects need to be initialized with an api key, see Access and Authentication for details.

The basic flow of running a quantum circuit in a blocking manner is

  1. Create a circuit to run.
  2. Create a cirq.ionq.Service with proper authentication and endpoints.
  3. Submit this circuit to run on the service and await the results of this call. (Or alternatively use asynchronous jobs and processing)
  4. Transform the results in a form that is most useful for your analysis.

Here is a simple example of this flow

import cirq
import cirq.ionq as ionq

# A circuit that applies a square root of NOT and then a measurement.
qubit = cirq.LineQubit(0)
circuit = cirq.Circuit(
    cirq.X(qubit)**0.5,            # Square root of NOT.
    cirq.measure(qubit, key='x')   # Measurement store in key 'x'

# Create a ionq.Service object.
# Replace API_KEY with your api key.
# Or alternatively if you have the IONQ_API_KEY environment
# variables set, you can omit specifying thee api_key parameters.
service = ionq.Service(api_key=API_KEY)

# Run a program against the service. This method will block execution
# until the result is returned and periodically polls the IonQ API.
result =, repetitions=100, target='qpu')

# The return object of run is a cirq.Result object.
# From this object you can get a histogram of results.
histogram = result.histogram(key='x')
print(f'Histogram: {histogram}')

# Or the data as a pandas frame.

This produces output (will vary due to quantum randomness!)

Histogram: Counter({0: 53, 1: 47})
0   0
1   0
2   0
3   0
4   0
.. ..
95  1
96  1
97  1
98  1
99  1

[100 rows x 1 columns]

Service options

In addition to the remote_host and api_key there are some other options which are useful for configuring the service. The most useful of these are

  • default_target: this is a string of either simulator or qpu. By setting this you do not have to specify a target every time you run a job using run, create_job or via the sampler interface. A helpful pattern is to create two services with defaults for the simulator and for the QPU separately.

  • max_retry_seconds: The API will pull with exponential backoff for completed jobs. By specifying this you can change the number of seconds before this retry gives up. It is common to set this to a very small number when, for example, wanting to fail fast, or to be set very long for long running jobs.

Next steps

Learn how to build circuits for the API

How to use the service API

Get information about QPUs from IonQ calibrations