Running with Threadpool

Qiskit Aer runs simulation jobs on a single-worker Python multiprocessing ThreadPool executor so that all parallelization is handled by low-level OpenMP and CUDA code. However to customize job-level parallel execution of multiple circuits a user can specify a custom multiprocessing executor and control the splitting of circuits using the executor and max_job_size backend options.

Usage of executor

To use Threadpool as an executor, you need to set executor and max_job_size by set_options function. If both executor (default None) and max_job_size (default None) are set, Aer splits the multiple circuits to some chunk of circuits and submits them to the executor. max_job_size can control the number of splitting circuits. When max_job_size is set to 1, multiple circuits are split into one circuit and distributed to the executor. If a user executes 60 circuits with the executor and max_job_size=1, Aer splits it as 60 jobs each of 1 circuit. If there are 60 circuits and max_job_size=2, Aer splits it as 30 jobs, each with 2 circuits.

Example: Threadpool execution

import qiskit
from concurrent.futures import ThreadPoolExecutor
from qiskit_aer import AerSimulator
from math import pi

# Generate circuit
circ = qiskit.QuantumCircuit(15, 15)
circ.h(0)
circ.cx(0, 1)
circ.cx(1, 2)
circ.p(pi/2, 2)
circ.measure([0, 1, 2], [0, 1 ,2])

circ2 = qiskit.QuantumCircuit(15, 15)
circ2.h(0)
circ2.cx(0, 1)
circ2.cx(1, 2)
circ2.p(pi/2, 2)
circ2.measure([0, 1, 2], [0, 1 ,2])
circ_list = [circ, circ2]

qbackend = AerSimulator()
# Set executor and max_job_size
exc = ThreadPoolExecutor(max_workers=2)
qbackend.set_options(executor=exc)
qbackend.set_options(max_job_size=1)
result = qbackend.run(circ_list).result()