`options` and `run_options` for the Sampler and Estimator classes

The Sampler and Estimator classes provided in the Quantum Rings toolkit provide a way to provide additional configuration parameters as a dictionary and passed on as options or run_options parameters. Supported parameters are listed below.

Parameter	Options	Description
token	a string value	Your account’s API key. Downloaded it from the “Manage Keys” section from <https://portal.quantumrings.com>
name	a string value	Your account name. The e-mail ID with which you created your Quantum Rings Account.
backend	One of the following: “scarlet_quantum_rings” “amber_quantum_rings” “serin_quantum_rings”	select from one of these three backends depending upon your installation
precision	“single” or “double”	The default is “single”. Select “double”, if you need high accuracy.
gpu	an integer >= 0	In a multiple GPU installation, you can provide the gpu_id to use. Usually, this is an integer >= 0, with 0 being the first GPU.
shots	an integer > 1	Sets the number of bitstrings to be sampled in the experiment
mode	“sync” or “async”	Selects whether to run the circuit in synchronous or asynchronous mode
performance	One of the following “HIGHESTEFFICIENCY” “BALANCEDACCURACY” “HIGHESTACCURACY” “AUTOMATIC” “CUSTOM”	Selects an internal threshold value for permance. If “CUSTOM” then a threshold value is to be provided
threshold	an integer > 16	Sets the threshold value of performance is set to “CUSTOM”
transfer_to_cpu	True or False	If using a GPU mode, selecting this to False will keep the operation in the GPU mode while performing measurements.
generate_amplitude	True or False	If set, generates amplitudes for the measured bitstrings. Supported only in `QrBackendV2.run` method. a file name must be provided.
file	a fully qualified string file name.	File for storing measurements and amplitudes. Supported only in `QrBackendV2.run` method.
max_threads	an integer > 1	Sets the maximum number of threads while performing measurements. Supported only in `QrBackendV2.run` method.

The following code fragment is a practical illustration of how to use these settings in your own work.

# Obtain the API key and account name from the OS environment, assuming the user has set so.

import os
my_token = os.environ["QR_TOKEN"]
my_name = os.environ["QR_ACCOUNT"]
my_backend = "scarlet_quantum_rings"

from quantumrings.toolkit.qiskit import QrRuntimeService
from quantumrings.toolkit.qiskit import QrSamplerV2 as Sampler

qr_services = QrRuntimeService(name = my_name, token = my_token)
qr_backend = qr_services.backend(name = my_backend)

sampler=Sampler(backend = qr_backend, options={"shots": 100, "precision": "double"})

# Rest of the code

Another example illustrating the use in a QrBackendV2.run method is as follows.

import os

my_token = os.environ["QR_TOKEN"]
my_name = os.environ["QR_ACCOUNT"]
my_backend = "scarlet_quantum_rings"

from qiskit.circuit import QuantumCircuit
from qiskit import QuantumCircuit, transpile, QuantumRegister, ClassicalRegister, AncillaRegister
from qiskit.visualization import plot_histogram
from matplotlib import pyplot as plt

from quantumrings.toolkit.qiskit import QrBackendV2
backend = QrBackendV2(token = my_token, name = my_name, backend = my_backend, num_qubits = 12)

shots = 10000
numberofqubits = backend.num_qubits
q = QuantumRegister(numberofqubits , 'q')
c = ClassicalRegister(numberofqubits , 'c')
qc = QuantumCircuit(q, c)

# Create the GHZ state (Greenberger-Horne-Zeilinger)
qc.h(0);
for i in range (qc.num_qubits - 1):
    qc.cx(i, i + 1);

# Measure all qubits
qc.measure_all();

run_parameters = {"shots": shots, "precision":"double", "max_threads":10}
job = backend.run(qc, **run_parameters)
result = job.result()
counts = result.get_counts()
plot_histogram(counts)

options and run_options for the Sampler and Estimator classes

`options` and `run_options` for the Sampler and Estimator classes