# Install Qiskit
pip install qiskit
pip install qiskit-aer
pip install qiskit-optimization
pip install qiskit-machine-learning

# Basic quantum circuit
from qiskit import QuantumCircuit, transpile, Aer, execute
from qiskit.visualization import plot_histogram
import matplotlib.pyplot as plt

# Create a quantum circuit
qc = QuantumCircuit(2, 2)

# Add quantum gates
qc.h(0)  # Hadamard gate on qubit 0
qc.cx(0, 1)  # CNOT gate (control=0, target=1)
qc.measure_all()

# Visualize the circuit
qc.draw('mpl')
plt.show()

# Execute on simulator
simulator = Aer.get_backend('qasm_simulator')
job = execute(qc, simulator, shots=1000)
result = job.result()
counts = result.get_counts(qc)
plot_histogram(counts)

# Install Cirq
pip install cirq

import cirq
import numpy as np

# Create quantum circuit
qubits = cirq.LineQubit.range(2)
circuit = cirq.Circuit()

# Add gates
circuit.append(cirq.H(qubits[0]))
circuit.append(cirq.CNOT(qubits[0], qubits[1]))
circuit.append(cirq.measure(*qubits, key='result'))

# Simulate
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=1000)
print(result.histogram(key='result'))

// Basic Q# program
namespace QuantumHelloWorld {
    open Microsoft.Quantum.Canon;
    open Microsoft.Quantum.Intrinsic;

    @EntryPoint()
    operation HelloQ() : Unit {
        Message("Hello, Quantum World!");
    }

    // Quantum teleportation
    operation Teleport(msg : Qubit, target : Qubit) : Unit {
        using (register = Qubit[2]) {
            let here = register[0];
            let there = register[1];
            
            // Create Bell pair
            H(here);
            CNOT(here, there);
            
            // Teleportation protocol
            CNOT(msg, here);
            H(msg);
            
            if (M(msg) == One) { Z(target); }
            if (M(here) == One) { X(target); }
        }
    }
}

from qiskit import QuantumCircuit, transpile, Aer, execute
from qiskit.visualization import plot_histogram
import numpy as np

def grover_algorithm(n_qubits, target_state):
    """Implement Grover's algorithm for database search"""
    
    # Create quantum circuit
    qc = QuantumCircuit(n_qubits, n_qubits)
    
    # Initialize superposition
    for i in range(n_qubits):
        qc.h(i)
    
    # Grover iterations
    iterations = int(np.pi/4 * np.sqrt(2**n_qubits))
    
    for _ in range(iterations):
        # Oracle (marks target state)
        oracle = create_oracle(n_qubits, target_state)
        qc = qc.compose(oracle)
        
        # Diffusion operator
        diffusion = create_diffusion(n_qubits)
        qc = qc.compose(diffusion)
    
    # Measure
    qc.measure_all()
    
    return qc

def create_oracle(n_qubits, target):
    """Create oracle that marks target state"""
    qc = QuantumCircuit(n_qubits)
    
    # Apply X gates to flip target state
    for i, bit in enumerate(target):
        if bit == '0':
            qc.x(i)
    
    # Apply multi-controlled Z gate
    if n_qubits > 1:
        qc.mcz(list(range(n_qubits-1)), n_qubits-1)
    
    # Apply X gates again to restore
    for i, bit in enumerate(target):
        if bit == '0':
            qc.x(i)
    
    return qc

def create_diffusion(n_qubits):
    """Create diffusion operator"""
    qc = QuantumCircuit(n_qubits)
    
    # Apply H gates
    for i in range(n_qubits):
        qc.h(i)
    
    # Apply X gates
    for i in range(n_qubits):
        qc.x(i)
    
    # Apply multi-controlled Z gate
    if n_qubits > 1:
        qc.mcz(list(range(n_qubits-1)), n_qubits-1)
    
    # Apply X gates again
    for i in range(n_qubits):
        qc.x(i)
    
    # Apply H gates again
    for i in range(n_qubits):
        qc.h(i)
    
    return qc

# Example usage
n_qubits = 3
target_state = '101'
grover_circuit = grover_algorithm(n_qubits, target_state)

# Execute
simulator = Aer.get_backend('qasm_simulator')
job = execute(grover_circuit, simulator, shots=1000)
result = job.result()
counts = result.get_counts(grover_circuit)
plot_histogram(counts)

def shor_algorithm(N):
    """Shor's algorithm for factoring N"""
    
    # Step 1: Choose random a < N
    import random
    a = random.randint(2, N-1)
    
    # Step 2: Check if gcd(a, N) > 1
    from math import gcd
    if gcd(a, N) > 1:
        return gcd(a, N)
    
    # Step 3: Find period r of a^x mod N
    r = find_period_quantum(a, N)
    
    # Step 4: Check if r is odd
    if r % 2 == 1:
        return shor_algorithm(N)
    
    # Step 5: Check if a^(r/2) ≡ -1 (mod N)
    if pow(a, r//2, N) == N-1:
        return shor_algorithm(N)
    
    # Step 6: Factors are gcd(a^(r/2) ± 1, N)
    factor1 = gcd(pow(a, r//2, N) + 1, N)
    factor2 = gcd(pow(a, r//2, N) - 1, N)
    
    return factor1, factor2

def find_period_quantum(a, N):
    """Find period using quantum period finding"""
    
    # Number of qubits needed
    n_qubits = int(np.ceil(np.log2(N)))
    
    # Create quantum circuit
    qc = QuantumCircuit(2*n_qubits, n_qubits)
    
    # Initialize superposition
    for i in range(n_qubits):
        qc.h(i)
    
    # Apply modular exponentiation
    qc = apply_modular_exponentiation(qc, a, N, n_qubits)
    
    # Apply inverse QFT
    qc = apply_inverse_qft(qc, n_qubits)
    
    # Measure
    qc.measure(range(n_qubits), range(n_qubits))
    
    # Execute and find period
    simulator = Aer.get_backend('qasm_simulator')
    job = execute(qc, simulator, shots=1000)
    result = job.result()
    counts = result.get_counts(qc)
    
    # Find most likely period
    period = find_most_likely_period(counts, N)
    
    return period

from qiskit import QuantumCircuit
from qiskit.circuit import Parameter
import numpy as np

class QuantumNeuralNetwork:
    def __init__(self, n_qubits, n_layers):
        self.n_qubits = n_qubits
        self.n_layers = n_layers
        self.parameters = []
        self.circuit = None
        
    def create_circuit(self):
        """Create parameterized quantum circuit"""
        qc = QuantumCircuit(self.n_qubits)
        
        # Initialize parameters
        params = []
        for layer in range(self.n_layers):
            for qubit in range(self.n_qubits):
                param = Parameter(f'θ_{layer}_{qubit}')
                params.append(param)
        
        self.parameters = params
        
        # Add parameterized gates
        for layer in range(self.n_layers):
            # Rotation gates
            for qubit in range(self.n_qubits):
                qc.ry(params[layer * self.n_qubits + qubit], qubit)
            
            # Entangling gates
            for qubit in range(self.n_qubits - 1):
                qc.cx(qubit, qubit + 1)
        
        self.circuit = qc
        return qc
    
    def forward(self, input_data):
        """Forward pass through quantum circuit"""
        # Encode input data
        qc = self.encode_data(input_data)
        
        # Add parameterized circuit
        qc = qc.compose(self.circuit)
        
        # Measure
        qc.measure_all()
        
        return qc
    
    def encode_data(self, data):
        """Encode classical data into quantum state"""
        qc = QuantumCircuit(self.n_qubits)
        
        # Amplitude encoding
        for i, value in enumerate(data[:self.n_qubits]):
            qc.ry(2 * np.arcsin(np.sqrt(value)), i)
        
        return qc

class QuantumSVM:
    def __init__(self, n_qubits):
        self.n_qubits = n_qubits
        self.circuit = None
        
    def create_feature_map(self, data):
        """Create quantum feature map"""
        qc = QuantumCircuit(self.n_qubits)
        
        # Encode data
        for i, value in enumerate(data):
            qc.ry(2 * value, i)
        
        # Add entangling layers
        for i in range(self.n_qubits - 1):
            qc.cx(i, i + 1)
        
        return qc
    
    def quantum_kernel(self, x1, x2):
        """Calculate quantum kernel between two data points"""
        # Create feature maps
        qc1 = self.create_feature_map(x1)
        qc2 = self.create_feature_map(x2)
        
        # Calculate overlap
        overlap = self.calculate_overlap(qc1, qc2)
        
        return overlap
    
    def calculate_overlap(self, qc1, qc2):
        """Calculate overlap between quantum states"""
        # Create circuit to measure overlap
        qc = QuantumCircuit(self.n_qubits + 1)
        
        # Prepare first state
        qc = qc.compose(qc1, range(self.n_qubits))
        
        # Prepare second state in ancilla
        qc.h(self.n_qubits)
        for i in range(self.n_qubits):
            qc.cx(i, self.n_qubits)
        
        # Measure ancilla
        qc.measure(self.n_qubits, 0)
        
        # Execute and calculate overlap
        simulator = Aer.get_backend('qasm_simulator')
        job = execute(qc, simulator, shots=1000)
        result = job.result()
        counts = result.get_counts(qc)
        
        # Calculate overlap from measurement results
        overlap = counts.get('0', 0) / 1000
        
        return overlap

def quantum_key_distribution():
    """BB84 quantum key distribution protocol"""
    
    # Alice's random bits
    alice_bits = np.random.randint(0, 2, 100)
    alice_bases = np.random.randint(0, 2, 100)
    
    # Bob's random bases
    bob_bases = np.random.randint(0, 2, 100)
    
    # Quantum channel simulation
    quantum_states = []
    for i in range(100):
        qc = QuantumCircuit(1)
        
        # Alice prepares qubit
        if alice_bits[i] == 1:
            qc.x(0)
        
        if alice_bases[i] == 1:
            qc.h(0)
        
        quantum_states.append(qc)
    
    # Bob measures
    bob_results = []
    for i, qc in enumerate(quantum_states):
        if bob_bases[i] == 1:
            qc.h(0)
        
        qc.measure(0, 0)
        
        # Simulate measurement
        simulator = Aer.get_backend('qasm_simulator')
        job = execute(qc, simulator, shots=1)
        result = job.result()
        counts = result.get_counts(qc)
        
        bob_results.append(int(list(counts.keys())[0]))
    
    # Sift key
    sifted_key = []
    for i in range(100):
        if alice_bases[i] == bob_bases[i]:
            sifted_key.append(alice_bits[i])
    
    return sifted_key

from qiskit.optimization import QuadraticProgram
from qiskit.optimization.algorithms import QAOA

def quantum_optimization():
    """Quantum approximate optimization algorithm"""
    
    # Create optimization problem
    qp = QuadraticProgram()
    qp.binary_var('x')
    qp.binary_var('y')
    qp.binary_var('z')
    
    # Define objective function
    qp.minimize(linear={'x': 1, 'y': 1, 'z': 1}, 
                quadratic={('x', 'y'): 2, ('y', 'z'): 2, ('x', 'z'): 2})
    
    # Solve using QAOA
    qaoa = QAOA(quantum_instance=Aer.get_backend('qasm_simulator'))
    result = qaoa.solve(qp)
    
    return result

from qiskit import IBMQ

# Load IBM Quantum account
IBMQ.load_account()
provider = IBMQ.get_provider()

# Get available backends
backends = provider.backends()
print("Available backends:")
for backend in backends:
    print(f"- {backend.name()}: {backend.status()}")

# Select quantum computer
quantum_computer = provider.get_backend('ibmq_qasm_simulator')

# Execute quantum circuit
job = execute(grover_circuit, quantum_computer, shots=1000)
result = job.result()

import cirq
import cirq_google

# Create quantum circuit
qubits = cirq.LineQubit.range(2)
circuit = cirq.Circuit()

# Add gates
circuit.append(cirq.H(qubits[0]))
circuit.append(cirq.CNOT(qubits[0], qubits[1]))
circuit.append(cirq.measure(*qubits, key='result'))

# Execute on Google's quantum computer
# (Requires Google Cloud Quantum AI setup)