Using the QPU-based stack

The broad strokes of working with the QPU-based pyQuil stack are identical to using the QVM-based stack: the library pyquil.api supplies an object class QPUConnection which mediates the transmission of Quil programs to the QPU, encoded as pyquil.quil.Program objects, as well as the receipt of job results, encoded as bitstring lists.

Note

User permissions for QPU access must be enabled by a Forest administrator. QPUConnection calls will automatically fail without these user permissions. Speak to a Forest administrator for information about upgrading your access plan.

Detecting the available QPUs and their structure

The initialization function for a QPUConnection object takes a QPU name as its sole argument. Devices are typically named according to the convention [n]Q-[name], where n is the number of active qubits on the device and name is a human-readable name that designates the device. The available QPUs can be inspected via a PyQuil interface, as demonstrated in the following snippet:

from pyquil.api import get_devices
for device in get_devices():
    if device.is_online():
        print('Device {} is online'.format(device.name))

The Device objects returned by get_devices will capture other characterizing statistics about the associated QPU at a later date.

Execution on the QPU

The user-facing interface to running Quil programs on the QPU is nearly identical to that of the QVM. A QPUConnection object provides the following methods:

  • .run(quil_program, classical_addresses, trials=1): This method sends the Program object quil_program to the QPU for execution, which runs the program trials many times. After each run on the QPU, all the qubits in the QPU are simultaneously measured and their results are stored in classical registers according to the MEASURE instructions provided. Then, a list of registers listed in classical_addresses is returned to the user for each trial. This call is blocking: it will wait until the QPU returns its results for inspection.
  • .run_async(quil_program, classical_addresses, trials=1): This method has identical behavior to .run except that it is nonblocking, and it instead returns a job ID string.
  • .run_and_measure(quil_program, qubits, trials=1): This method sends the Program object quil_program to the QPU for execution, which runs the program trials many times. After each run on the QPU, the all the qubits in the QPU are simultaneously measured, and the results from those listed in qubits are returned to the user for each trial. This call is blocking: it will wait until the QPU returns its results for inspection.
  • .run_and_measure_async(quil_program, qubits, trials=1): This method has identical behavior to .run_and_measure except that it is nonblocking, and it instead returns a job ID string.

Note

The QPU’s run functionality matches that of the QVM’s run functionality, but the behavior of run_and_measure does not match its QVMConnection counterpart (cf. Optimized Calls). The QVMConnection version of run repeats the execution of a program many times, producing a (potentially) different outcome each time, whereas run_and_measure executes a program only once and uses the QVM’s unique ability to perform wavefunction inspection to multiply sample the same distribution. The QPU does not have this ability, and thus its run_and_measure call behaves as the QVM’s run.

For example, the following Python snippet demonstrates the execution of a small job on the QPU identified as “19Q-Acorn”:

from pyquil.quil import Program
import pyquil.api as api
from pyquil.gates import *
qpu = api.QPUConnection('19Q-Acorn')
p = Program(H(0), CNOT(0, 1), MEASURE(0, 0), MEASURE(1, 1))
qpu.run(p, [0, 1], 1000)

When the QPU execution time is expected to be long and there is classical computation that the program would like to accomplish in the meantime, the QPUConnection object allows for an asynchronous run_async call to be placed instead. By storing the resulting job ID, the state of the job and be queried later and its results obtained then. The mechanism for querying the state of a job is also through the QPUConnection object: a job ID string can be transformed to a pyquil.api.Job object via the method .get_job(job_id); the state of a Job object (taken at its creation time) can then be inspected by the method .is_done(); and when this returns True the output of the QPU can be retrieved via the method .result().

For example, consider the following Python snippet:

from pyquil.quil import Program
import pyquil.api as api
from pyquil.gates import *
qpu = api.QPUConnection('19Q-Acorn')
p = Program(H(0), CNOT(0, 1), MEASURE(0, 0), MEASURE(1, 1))
job_id = qpu.run_async(p, [0, 1], 1000)
while not qpu.get_job(job_id).is_done():
    ## get some other work done while we wait
    ...
    ## and eventually yield to recheck the job result
## now the job is guaranteed to be finished, so pull the QPU results
job_result = qpu.get_job(job_id).result()

Simulating the QPU using the QVM

The QVM is a powerful tool for testing quantum programs before executing them on the QPU. In addition to the noise.py module for generating custom noise models for simulating noise on the QVM, pyQuil provides a simple interface for loading the QVM with noise models tailored to Rigetti’s available QPUs, in just one modified line of code. This is made possible via the Device class, which holds hardware specification information, noise model information, and instruction set architecture (ISA) information regarding connectivity. This information is held in the Specs, ISA and NoiseModel attributes of the Device class, respectively. To read more about the Device class, see here: The Device class.

Specifically, to load a QVM with the NoiseModel information from a Device, all that is required is to provide a Device object to the QVM during initialization:

from pyquil.api import get_devices, QVMConnection

acorn = get_devices(as_dict=True)['19Q-Acorn']
qvm = QVMConnection(acorn)

By simply providing a device during QVM initialization, all programs executed on this QVM will, by default, have noise applied that is characteristic of the corresponding Rigetti QPU (in the case above, the acorn device). One may then efficiently test realistic quantum algorithms on the QVM, in advance of running those programs on the QPU.

Retune interruptions

Because the QPU is a physical device, it is occasionally taken offline for recalibration. This offline period typically lasts 10-40 minutes, depending upon QPU characteristics and other external factors. During this period, the QPU will be listed as offline, and it will reject new jobs (but pending jobs will remain queued). When the QPU resumes activity, its performance characteristics may be slightly different (in that different gates may enjoy different process fidelities).