[ Сураба 🦀]

rs.initiate(
{
_id: "myReplicaSet",
members: [
{ _id: 0, host: "node1:27017" },
{ _id: 1, host: "node2:27017" },
{ _id: 2, host: "node3:27017" }
]
}
)

rs.status()

min.insync.replicas=2

acks=all

rs.initiate(
{
_id: "myReplicaSet",
members: [
{ _id: 0, host: "node1:27017" },
{ _id: 1, host: "node2:27017" },
{ _id: 2, host: "node3:27017" }
]
}
)

rs.status()

min.insync.replicas=2

acks=all

p = gcd(x + 1, N)
        q = gcd(x - 1, N)
        
        if p * q != N:
            return ShorsResult(
                factors=(1, N),
                quantum_circuit=circuit,
                measurement_results=counts,
                period=period,
                success=False,
                error_message="Invalid factorization"
            )

        return ShorsResult(
            factors=(p, q),
            quantum_circuit=circuit,
            measurement_results=counts,
            period=period,
            success=True
        )

# Example usage
def main():
    # Example factorization
    N = 15
    shor = ImprovedShorsAlgorithm(shots=1000)
    
    try:
        result = shor.factor(N)
        
        if result.success:
            logger.info(f"Successfully factored {N} = {result.factors[0]} × {result.factors[1]}")
            logger.info(f"Found period: {result.period}")
            logger.info(f"Circuit depth: {result.quantum_circuit.depth()}")
            logger.info(f"Number of qubits: {result.quantum_circuit.num_qubits}")
        else:
            logger.error(f"Factorization failed: {result.error_message}")
            
    except Exception as e:
        logger.error(f"An error occurred: {str(e)}")

if __name__ == "__main__":
    main()

"""
        Process measurement results to find the period
        
        Args:
            counts: Measurement results
            N: Number to factor
        Returns:
            Tuple[int, bool]: (period, success flag)
        """
        max_result = max(counts, key=counts.get)
        measured_phase = int(max_result, 2) / (2 ** len(max_result))
        
        # Use continued fractions to find r
        fraction = self._continued_fraction_expansion(measured_phase)
        candidates = self._find_closest_fractions(fraction, N)
        
        for denominator in candidates:
            if denominator % 2 == 0:
                return denominator, True
        
        return 0, False

    def _continued_fraction_expansion(self, x: float, depth: int = 10) -> List[int]:
        """
        Compute continued fraction expansion
        
        Args:
            x: Input float
            depth: Maximum depth of expansion
        Returns:
            List[int]: Continued fraction coefficients
        """
        fractions = []
        for _ in range(depth):
            integer_part = int(x)
            fractions.append(integer_part)
            fractional_part = x - integer_part
            if abs(fractional_part) < 1e-10:
                break
            x = 1 / fractional_part
        return fractions

    def _find_closest_fractions(self, fractions: List[int], N: int) -> List[int]:
        """
        Find closest fractions from continued fraction expansion
        
        Args:
            fractions: Continued fraction coefficients
            N: Number to factor
        Returns:
            List[int]: List of candidate denominators
        """
        convergents = [(1, 0), (fractions[0], 1)]
        candidates = []
        
        for i in range(1, len(fractions)):
            num = fractions[i] * convergents[-1][0] + convergents[-2][0]
            den = fractions[i] * convergents[-1][1] + convergents[-2][1]
            
            if den < N:
                convergents.append((num, den))
                candidates.append(den)
            else:
                break
                
        return candidates

    def factor(self, N: int) -> ShorsResult:
        """
        Main method to factor a number using Shor's algorithm
        
        Args:
            N: Number to factor
        Returns:
            ShorsResult: Result object containing factors and metadata
        """
        try:
            self._validate_input(N)
        except ValueError as e:
            return ShorsResult(
                factors=(1, N),
                quantum_circuit=None,
                measurement_results={},
                period=0,
                success=False,
                error_message=str(e)
            )

        # Choose random number coprime to N
        a = 2  # Start with smallest value for simplicity
        while gcd(a, N) != 1:
            a += 1

        # Create and execute quantum circuit
        circuit = self._quantum_phase_estimation(a, N)
        transpiled_circuit = transpile(circuit, self.simulator, 
                                     optimization_level=self.optimize_level)
        
        job = self.simulator.run(transpiled_circuit, shots=self.shots)
        counts = job.result().get_counts()

        # Find period from measurements
        period, success = self._process_measurement(counts, N)
        
        if not success:
            return ShorsResult(
                factors=(1, N),
                quantum_circuit=circuit,
                measurement_results=counts,
                period=period,
                success=False,
                error_message="Failed to find valid period"
            )

        # Classical post-processing
        x = pow(a, period // 2, N)
        if x == 1 or x == N - 1:
            return self.factor(N)  # Try again

import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, transpile, Aer, assemble
from qiskit.visualization import plot_histogram
from qiskit.circuit.library import QFT
from qiskit.providers.aer import AerSimulator
from typing import Tuple, List, Optional
import logging
from math import gcd, ceil, log2
from dataclasses import dataclass

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class ShorsResult:
    """Data class to store Shor's algorithm results"""
    factors: Tuple[int, int]
    quantum_circuit: QuantumCircuit
    measurement_results: dict
    period: int
    success: bool
    error_message: Optional[str] = None

class ImprovedShorsAlgorithm:
    def __init__(self, shots: int = 1000, optimize_level: int = 3):
        """
        Initialize Shor's Algorithm with configurable parameters
        
        Args:
            shots: Number of shots for quantum measurement
            optimize_level: Optimization level for circuit transpilation
        """
        self.shots = shots
        self.optimize_level = optimize_level
        self.simulator = AerSimulator()

    def _validate_input(self, N: int) -> bool:
        """
        Validate the input number N
        
        Args:
            N: Number to be factored
        Returns:
            bool: True if input is valid
        """
        if N < 3:
            raise ValueError("Number must be greater than 2")
        if N % 2 == 0:
            raise ValueError("Number must be odd")
        if ceil(log2(N)) != floor(log2(N)):
            return True
        return False

    def _create_controlled_unitary(self, a: int, N: int, n_count: int) -> QuantumCircuit:
        """
        Create the controlled unitary operation for QPE
        
        Args:
            a: Random coprime number
            N: Number to factor
            n_count: Number of counting qubits
        Returns:
            QuantumCircuit: Controlled unitary circuit
        """
        qr = QuantumRegister(n_count + ceil(log2(N)), 'q')
        circuit = QuantumCircuit(qr)

        # Implement modular exponentiation
        for i in range(n_count):
            power = pow(a, 2**i, N)
            for j in range(ceil(log2(N))):
                if (power >> j) & 1:
                    circuit.cx(qr[i], qr[n_count + j])

        return circuit

    def _quantum_phase_estimation(self, a: int, N: int) -> QuantumCircuit:
        """
        Improved Quantum Phase Estimation implementation
        
        Args:
            a: Random coprime number
            N: Number to factor
        Returns:
            QuantumCircuit: Complete QPE circuit
        """
        n_count = 2 * ceil(log2(N))  # Increased precision
        n_register = ceil(log2(N))
        
        # Create registers
        qr_count = QuantumRegister(n_count, 'count')
        qr_register = QuantumRegister(n_register, 'register')
        cr = ClassicalRegister(n_count, 'c')
        
        circuit = QuantumCircuit(qr_count, qr_register, cr)

        # Initialize register
        circuit.x(qr_register[0])  # Initialize |1⟩ state
        
        # Apply Hadamard gates
        for i in range(n_count):
            circuit.h(qr_count[i])

        # Apply controlled unitary operations
        controlled_unitary = self._create_controlled_unitary(a, N, n_count)
        circuit.compose(controlled_unitary, inplace=True)

        # Apply inverse QFT
        inverse_qft = QFT(n_count).inverse()
        circuit.compose(inverse_qft, qubits=range(n_count), inplace=True)

        # Measure counting qubits
        circuit.measure(qr_count, cr)

        return circuit

    def _process_measurement(self, counts: dict, N: int) -> Tuple[int, bool]:

sudo apt install iproute2 # For Debian/Ubuntu
sudo yum install iproute # For CentOS/RHEL

sudo ip link add link eth0 name eth0.10 type vlan id 10

sudo ip link add link eth0 name eth0.20 type vlan id 20

sudo ip addr add 192.168.10.1/24 dev eth0.10
sudo ip addr add 192.168.20.1/24 dev eth0.20

sudo ip link set eth0.10 up 
sudo ip link set eth0.20 up 

ip -d link show 

sudo ip link add name br-vlan type bridge

sudo ip link set eth0.10 master br-vlan

sudo ip link set eth0.20 master br-vlan

sudo ip addr add 192.168.30.1/24 dev br-vlan
sudo ip link set br-vlan up

sudo ip link delete eth0.10
sudo ip link delete eth0.20

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