Quantum Machine Learning

What is Quantum ML? Using quantum computers for machine learning tasks, potentially offering speedups for certain algorithms.

Quantum Computing Basics

Quantum Hardware

QML Approaches

Variational Quantum Circuits

import pennylane as qml

dev = qml.device("default.qubit", wires=4)

@qml.qnode(dev)
def quantum_classifier(inputs, weights):
    # Encode classical data
    qml.AngleEmbedding(inputs, wires=range(4))

    # Parameterized quantum layers
    qml.StronglyEntanglingLayers(weights, wires=range(4))

    # Measurement
    return qml.expval(qml.PauliZ(0))

# Train like classical NN
optimizer = qml.GradientDescentOptimizer()
for epoch in range(100):
    weights = optimizer.step(cost_fn, weights)

Quantum Kernels Use quantum computer to compute kernel for SVM:

from qiskit_machine_learning.kernels import FidelityQuantumKernel

kernel = FidelityQuantumKernel(feature_map=ZZFeatureMap(4))
svc = SVC(kernel=kernel.evaluate)
svc.fit(X_train, y_train)

Current Limitations

Realistic Timeline

Where to Experiment

Best Practices

• Great for research/learning
• Use hybrid classical-quantum approaches
• Start with simulators
• Watch for practical advantages
• Consider for specific algorithms (optimization)