Quantum Machine Learning Research

Exploring the intersection of quantum computing and machine learning through interactive experiments.

Quantum Circuit Sim (5 Qubits | GHZ State)

Visualize quantum circuits and machine learning model training in real-time simulation.

Key Concepts

Quantum Neural Networks
Quantum Feature Encoding
Qubit Optimization

Current Projects

Quantum Pattern Recognition

Training quantum circuits for non-linear pattern recognition.

Quantum Entanglement Sim

Simulating entanglement-based machine learning models.

Variational Algorithms

Optimizing quantum gates for ML applications.

import qiskit
from qiskit.visualization import plot_histogram
from qiskit import QuantumCircuit, transpile, Aer, execute

def quantum_model(data):
    qc = QuantumCircuit(4)
    for i in range(4):
        qc.h(i)
    qc.barrier()
    
    # Quantum feature encoding
    qc.rx(data[0], 0)
    qc.ry(data[1], 1)
    qc.rz(data[2], 2)
    qc.rz(data[3], 3)
    qc.barrier()
    
    # Entanglement for ML
    qc.h(2)
    qc.cx(0, 1)
    qc.cz(2, 3)
    
    return execute(qc, backend=Aer.get_backend('qasm_simulator'), 
                  shots=1024, optimization_level=3).result().get_counts()

Quantum ML Experiments

// Quantum machine learning model const result = quantum_model(input_data) plot_histogram(result)

Test quantum algorithms with real-world datasets and observe results.

Dive into the Quantum Future

Explore quantum's potential for machine learning and AI innovation!