Compile and run quantum simulation on devices

We demonstrate how to employ SimuQ to simulate the 3-site Ising model on IonQ device through IonQ cloud. A more detailed discussion is in Simulate Ising model on multiple platforms.

Install dependencies

To activate IonQ Quantum Cloud in SimuQ, we need to install several optional dependencies. This can be done by executing

pip install simuq[ionq]

Declare IonQ providers

Accesses to IonQ Quantum Cloud require an API key from IonQ. It directly gives you access to IonQ’s noisy and ideal simulators for free.

Assuming that you have a string API_key storing it, we can declare an IonQ provider in SimuQ by

from simuq.ionq import IonQProvider
iqp = IonQProvider(API_key)

Alternatively, you can also store the key in a file and pass the path to the file in the delcaration of the provider:

iqp = IonQProvider(from_file = './API_key')

Compile, run, and retrieve results

To compile and run the 3-site Ising system qs in programming an Ising model on IonQ devices, we need to specify a device and an AAIS. By default, the device is Aria-1 and the AAIS in compilation is Heisenberg-AAIS. One can also pass detailed parameters in the compilation like error tolerance bound and number of Trotterization here. For simplicity, we execute with the default compiler configuration by

iqp.compile(qs)

We then run the experiment on simulator, which executes

iqp.run(on_simulator = True)

This will submit a job to IonQ Quantum Cloud using the compiled results, and print the job id and status. The simulator will account for the noise model of Aria-1. To retrieve the results, we wait for a short period of time (normally several seconds) for the cloud server to queue and process the simulation, and then run

iqp.results()

This returns a dictionary, whose results may depend on calibration data changing by date. In our experiments, it returns

{'000': 0.609375,
 '011': 0.1042480469,
 '101': 0.2021484375,
 '110': 0.08422851562}

To run on real devices, we can execute

iqp.run(shots = 4096)

Here shots represent the number of repetition of the experiment. After queuing and executing, you may retrieve the results by calling iqp.results().