quantum-expert

from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit import Aer, execute, transpile
from qiskit.visualization import plot_histogram, plot_bloch_multivector
import numpy as np
 
# Basic Quantum Circuit
def create_bell_state():
    """Create Bell state (maximally entangled state)"""
    qc = QuantumCircuit(2, 2)
 
    # Create superposition on qubit 0
    qc.h(0)
 
    # Entangle qubits 0 and 1
    qc.cx(0, 1)
 
    # Measure both qubits
    qc.measure([0, 1], [0, 1])
 
    return qc
 
# Quantum Teleportation
def quantum_teleportation():
    """Implement quantum teleportation protocol"""
    qc = QuantumCircuit(3, 3)
 
    # Prepare state to teleport (qubit 0)
    qc.ry(np.pi/4, 0)
 
    # Create Bell pair between qubits 1 and 2
    qc.h(1)
    qc.cx(1, 2)
 
    # Bell measurement on qubits 0 and 1
    qc.cx(0, 1)
    qc.h(0)
    qc.measure([0, 1], [0, 1])
 
    # Apply corrections on qubit 2 based on measurement
    qc.cx(1, 2)
    qc.cz(0, 2)
 
    # Measure final state
    qc.measure(2, 2)
 
    return qc
 
# Grover's Search Algorithm
class GroverSearch:
    def __init__(self, n_qubits: int, marked_state: str):
        self.n_qubits = n_qubits
        self.marked_state = marked_state
        self.circuit = None
 
    def create_oracle(self):
        """Create oracle that marks the target state"""
        oracle = QuantumCircuit(self.n_qubits)
 
        # Mark the target state by flipping phase
        for i, bit in enumerate(reversed(self.marked_state)):
            if bit == '0':
                oracle.x(i)
 
        # Multi-controlled Z gate
        oracle.h(self.n_qubits - 1)
        oracle.mcx(list(range(self.n_qubits - 1)), self.n_qubits - 1)
        oracle.h(self.n_qubits - 1)
 
        # Uncompute
        for i, bit in enumerate(reversed(self.marked_state)):
            if bit == '0':
                oracle.x(i)
 
        return oracle
 
    def create_diffuser(self):
        """Create diffusion operator"""
        diffuser = QuantumCircuit(self.n_qubits)
 
        # Apply H gates
        diffuser.h(range(self.n_qubits))
 
        # Apply X gates
        diffuser.x(range(self.n_qubits))
 
        # Multi-controlled Z
        diffuser.h(self.n_qubits - 1)
        diffuser.mcx(list(range(self.n_qubits - 1)), self.n_qubits - 1)
        diffuser.h(self.n_qubits - 1)
 
        # Apply X gates
        diffuser.x(range(self.n_qubits))
 
        # Apply H gates
        diffuser.h(range(self.n_qubits))
 
        return diffuser
 
    def build_circuit(self):
        """Build complete Grover's algorithm circuit"""
        self.circuit = QuantumCircuit(self.n_qubits, self.n_qubits)
 
        # Initialize in superposition
        self.circuit.h(range(self.n_qubits))
 
        # Calculate optimal number of iterations
        n_iterations = int(np.pi / 4 * np.sqrt(2**self.n_qubits))
 
        oracle = self.create_oracle()
        diffuser = self.create_diffuser()
 
        # Apply Grover iteration
        for _ in range(n_iterations):
            self.circuit.compose(oracle, inplace=True)
            self.circuit.compose(diffuser, inplace=True)
 
        # Measure
        self.circuit.measure(range(self.n_qubits), range(self.n_qubits))
 
        return self.circuit
 
    def run(self, shots: int = 1024):
        """Execute circuit"""
        backend = Aer.get_backend('qasm_simulator')
        job = execute(self.circuit, backend, shots=shots)
        result = job.result()
        counts = result.get_counts()
 
        return counts

from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import SLSQP
from qiskit.circuit.library import TwoLocal
from qiskit.primitives import Estimator
from qiskit.quantum_info import SparsePauliOp
 
class VQESolver:
    """Variational Quantum Eigensolver for finding ground state energy"""
 
    def __init__(self, hamiltonian: SparsePauliOp, n_qubits: int):
        self.hamiltonian = hamiltonian
        self.n_qubits = n_qubits
 
    def create_ansatz(self, reps: int = 2):
        """Create parameterized quantum circuit (ansatz)"""
        ansatz = TwoLocal(
            self.n_qubits,
            'ry',
            'cz',
            reps=reps,
            entanglement='linear'
        )
        return ansatz
 
    def run_vqe(self):
        """Run VQE algorithm"""
        ansatz = self.create_ansatz()
        optimizer = SLSQP(maxiter=100)
        estimator = Estimator()
 
        vqe = VQE(estimator, ansatz, optimizer)
        result = vqe.compute_minimum_eigenvalue(self.hamiltonian)
 
        return {
            "eigenvalue": result.eigenvalue,
            "optimal_parameters": result.optimal_parameters,
            "optimal_point": result.optimal_point,
            "cost_function_evals": result.cost_function_evals
        }
 
# Example: H2 molecule
def create_h2_hamiltonian():
    """Create Hamiltonian for H2 molecule"""
    # Simplified Hamiltonian
    hamiltonian = SparsePauliOp.from_list([
        ("II", -1.0523732),
        ("IZ", 0.39793742),
        ("ZI", -0.39793742),
        ("ZZ", -0.01128010),
        ("XX", 0.18093119)
    ])
    return hamiltonian

from qiskit_machine_learning.algorithms import VQC
from qiskit_machine_learning.neural_networks import CircuitQNN
from qiskit.circuit import Parameter
import numpy as np
 
class QuantumClassifier:
    """Variational Quantum Classifier"""
 
    def __init__(self, n_features: int, n_classes: int):
        self.n_features = n_features
        self.n_classes = n_classes
        self.vqc = None
 
    def create_feature_map(self):
        """Create feature map to encode classical data"""
        qc = QuantumCircuit(self.n_features)
 
        for i in range(self.n_features):
            param = Parameter(f'x[{i}]')
            qc.ry(param, i)
 
        return qc
 
    def create_ansatz(self):
        """Create parameterized circuit"""
        ansatz = TwoLocal(
            self.n_features,
            ['ry', 'rz'],
            'cz',
            reps=2,
            entanglement='full'
        )
        return ansatz
 
    def train(self, X_train, y_train):
        """Train quantum classifier"""
        feature_map = self.create_feature_map()
        ansatz = self.create_ansatz()
 
        self.vqc = VQC(
            num_qubits=self.n_features,
            feature_map=feature_map,
            ansatz=ansatz,
            optimizer=SLSQP(maxiter=100)
        )
 
        self.vqc.fit(X_train, y_train)
 
    def predict(self, X_test):
        """Predict using trained model"""
        return self.vqc.predict(X_test)