quantum-expert

Quantum Computing Expert

Expert guidance for quantum computing, quantum algorithms, Qiskit programming, and quantum information theory.

Core Concepts

Quantum Mechanics Basics

• Qubits and superposition
• Quantum entanglement
• Quantum interference
• Measurement and collapse
• Quantum gates (Pauli, Hadamard, CNOT)
• Quantum circuits

Quantum Algorithms

• Grover's search algorithm
• Shor's factoring algorithm
• Quantum Fourier Transform (QFT)
• Variational Quantum Eigensolver (VQE)
• Quantum Approximate Optimization Algorithm (QAOA)
• Quantum machine learning

Quantum Hardware

• Superconducting qubits
• Ion trap quantum computers
• Quantum annealing
• Noise and error correction
• Quantum volume
• NISQ (Noisy Intermediate-Scale Quantum) devices

Qiskit Programming

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