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Project description
Quantum Serverless client
Installation
pip install quantum_serverless
Documentation
Full docs can be found at https://qiskit-extensions.github.io/quantum-serverless/
Usage
Step 1: write pattern
from quantum_serverless import distribute_task, get, get_arguments, save_result
from qiskit import QuantumCircuit
from qiskit.circuit.random import random_circuit
from qiskit.primitives import Sampler
from qiskit.quantum_info import SparsePauliOp
# 1. let's annotate out function to convert it
# to distributed async function
# using `distribute_task` decorator
@distribute_task()
def distributed_sample(circuit: QuantumCircuit):
"""Calculates quasi dists as a distributed function."""
return Sampler().run(circuit).result().quasi_dists[0]
# 2. our program will have one arguments
# `circuits` which will store list of circuits
# we want to sample in parallel.
# Let's use `get_arguments` funciton
# to access all program arguments
arguments = get_arguments()
circuits = arguments.get("circuits", [])
# 3. run our functions in a loop
# and get execution references back
function_references = [
distributed_sample(circuit)
for circuit in circuits
]
# 4. `get` function will collect all
# results from distributed functions
collected_results = get(function_references)
# 5. `save_result` will save results of program execution
# so we can access it later
save_result({
"quasi_dists": collected_results
})
Step 2: run pattern
from quantum_serverless import ServerlessProvider, QiskitPattern
from qiskit.circuit.random import random_circuit
serverless = ServerlessProvider(
username="<USERNAME>",
password="<PASSWORD>",
host="<GATEWAY_ADDRESS>",
)
# create program
program = QiskitPattern(
title="Quickstart",
entrypoint="pattern.py",
working_dir="./src"
)
# create inputs to our program
circuits = []
for _ in range(3):
circuit = random_circuit(3, 2)
circuit.measure_all()
circuits.append(circuit)
# run program
job = serverless.run(
program=program,
arguments={
"circuits": circuits
}
)
Step 3: monitor job status
job.status()
# 'DONE'
# or get logs
job.logs()
Step 4: get results
job.result()
# {"quasi_dists": [
# {"0": 0.25, "1": 0.25, "2": 0.2499999999999999, "3": 0.2499999999999999},
# {"0": 0.1512273969460124, "1": 0.0400459556274728, "6": 0.1693190975212014, "7": 0.6394075499053132},
# {"0": 0.25, "1": 0.25, "4": 0.2499999999999999, "5": 0.2499999999999999}
# ]}
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