qlep 0.1.2

Description:

qlep 0.1.2

Quantum Leader Election Protocols (QLEP)
Description
The current project propose a template for developing and testing quantum leader election protocols. Most of the research protocols are implemented and can be compared with new proposals.
The scope of the project is to contain all the protocols in a single repository and to be able to compare them on the same benchmarks.
Install
pip install qlep

Use of AWS provider
Follow instructions https://aws.amazon.com/blogs/quantum-computing/setting-up-your-local-development-environment-in-amazon-braket/
export AWS_ACCESS_KEY_ID=YOUR_AWS_ACCESS_KEY_ID
export AWS_SECRET_ACCESS_KEY=YOUR_AWS_SECRET_ACCESS_KEY
export AWS_DEFAULT_REGION=us-west-1

Use of IBM provider
Follow instructions https://github.com/Qiskit/qiskit-ibm-provider
from qiskit_ibm_provider import IBMProvider
IBMProvider.save_account('YOUR_IBM_TOKEN')

Latex for plots
sudo apt-get install -y texlive-latex-base
sudo apt-get install -y texlive-latex-extra
sudo apt-get install -y dvipng
sudo apt-get install -y cm-super

Usage
import qlep

qdp=qlep.core.QuantumDataProvider(provider=qlep.core.Provider.AER, backend_name="aer_simulator")
committee=qlep.core.CommitteeType.ALL.get_committee(no_nodes=8, committee_size=8)
current_qlep=qlep.ecc.WalshQLEP(no_nodes=8, no_elections=10, quantum_data_provider=qdp, committee=committee)
current_qlep.generate_quantum_data()
current_qlep.simulate_elections()

# only if latex is installed
current_qlep.draw_boxplot(
simulate_file_name=current_qlep.get_simulate_file_name(),
draw_directory="plots"
)

Test your proposed protocol
import qlep
# numpy
import numpy as np
# qiskit
import qiskit
# override decorator
from typing_extensions import override


class NQLEP(qlep.core.QuantumLeaderElectionProtocolwithPoS):
r"""
An example class for a dummy quantum leader election protocol
"""
def __init__(
self,
no_nodes: int,
no_elections: int = 1,
quantum_data_provider: qlep.core.QuantumDataProvider = None,
committee: qlep.core.Committee = None,
) -> None:
super().__init__(
election_type=qlep.core.ElectionType.NEWPROTOCOL,
no_nodes=no_nodes,
no_elections=no_elections,
quantum_data_provider=quantum_data_provider,
committee=committee
)

@override
def get_quantum_circuits(
self,
measure: bool = True
) -> list[qiskit.QuantumCircuit]:
return [NQLEPStateGenerator.get_quantum_circuits(
no_nodes=self.quantum_no_nodes,
measure=measure
)]

@override
def get_leader_election_algorithm(
self
) -> qlep.core.LeaderElectionAlgorithm:
return NQLEPLeaderElectionAlgorithm()

@override
def get_malicious_attacker(self) -> qlep.core.MaliciousAttacker:
return NQLEPMaliciousAttacker()


class NQLEPStateGenerator:
@staticmethod
def get_quantum_circuits(
no_nodes: int,
measure: bool = True
) -> qiskit.QuantumCircuit:
# the number of qubits
no_qubits = no_nodes
# the qubits register
quantum_registers = qiskit.QuantumRegister(no_qubits, 'q')
if measure:
# the classic registers
classic_registers = qiskit.ClassicalRegister(no_qubits, 'c')
# the quantum circuit which use the qubits
# and the classic bits registers
quantum_circuit = qiskit.QuantumCircuit(quantum_registers,
classic_registers)
else:
quantum_circuit = qiskit.QuantumCircuit(quantum_registers)

# create the superposition for the first two qubits
quantum_circuit.h(quantum_registers[0])
quantum_circuit.cx(quantum_registers[0], quantum_registers[1])
quantum_circuit.x(quantum_registers[0])
quantum_circuit.barrier()
if measure:
quantum_circuit.measure(quantum_registers, classic_registers)
return quantum_circuit


class NQLEPLeaderElectionAlgorithm(qlep.core.LeaderElectionAlgorithm):
def __init__(self) -> None:
super().__init__()

@override
def elect(self, data: np.ndarray) -> int:
match (data[0, 0, 0], data[0, 0, 1]):
case (1, 0):
return 0
case (0, 1):
return 1
case _:
return -1


class NQLEPMaliciousAttacker(qlep.core.MaliciousAttacker):
def __init__(self) -> None:
super().__init__()

@override
def attack(
self,
register_ids: np.ndarray,
data: np.ndarray,
malicious_ids: np.ndarray
) -> np.ndarray:
# get the malicious nodes
malicious_nodes = [x for x in register_ids if x in malicious_ids]
# if no malicious nodes under our control than return the given data
if not malicious_nodes:
return data
# init return data
modified_data = np.copy(data)
# verify if the first two nodes are malicious
match (register_ids[0] in malicious_ids,
register_ids[1] in malicious_ids):
case (True, False):
modified_data[0, 0, 0] = 1
case (False, True):
modified_data[0, 0, 1] = 1
case (True, True):
modified_data[0, 0, 0] = 1
modified_data[0, 0, 1] = 0
case _:
pass
return modified_data

Credits
List of colaborators:

Stefan-Dan Ciocirlan
Dumitrel Loghin

License
Distributed under the EUPL v1.2 License. See LICENSE.txt for more information.