Introduction to Quantum Machine Learning

1 February 2025 · 2 min read · Syed Omar Faruk Towaha

Quantum Machine Learning (QML) combines the power of quantum computing with machine learning algorithms to solve problems that are intractable for classical computers.

What is Quantum Computing?

Quantum computers leverage quantum mechanical phenomena like superposition and entanglement. Unlike classical bits (0 or 1), quantum bits (qubits) can exist in superposition of both states simultaneously.

Why QML?

Exponential speedup: Quantum algorithms can process certain tasks exponentially faster
Enhanced feature spaces: Quantum states provide exponentially large feature spaces
Optimization: Quantum annealing for complex optimization problems
Pattern recognition: Quantum algorithms for pattern matching and classification

Key QML Algorithms

Several quantum algorithms show promise for ML applications:

Variational Quantum Eigensolver (VQE): For finding ground states
Quantum Approximate Optimization Algorithm (QAOA): For combinatorial optimization
Quantum Support Vector Machines: For classification tasks
Quantum Neural Networks: Parameterized quantum circuits for learning

Getting Started with Qiskit

IBM's Qiskit is an open-source framework for quantum computing:

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

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

# Apply Hadamard gate to create superposition
qc.h(0)

# Apply CNOT gate to create entanglement
qc.cx(0, 1)

# Measure qubits
qc.measure([0, 1], [0, 1])

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

print(counts)
# Output: {'00': ~500, '11': ~500}

Quantum Feature Maps

Quantum feature maps encode classical data into quantum states:

from qiskit.circuit.library import ZZFeatureMap
from qiskit_machine_learning.kernels import QuantumKernel

# Create feature map
feature_map = ZZFeatureMap(feature_dimension=2, reps=2)

# Create quantum kernel
qkernel = QuantumKernel(feature_map=feature_map)

# Use with sklearn
from sklearn.svm import SVC

# Train classifier
clf = SVC(kernel=qkernel.evaluate)
clf.fit(X_train, y_train)

# Predict
predictions = clf.predict(X_test)

Current Limitations

QML is still in its early stages with several challenges:

Noise: Current quantum computers are noisy (NISQ era)
Limited qubits: Small number of available qubits
Decoherence: Quantum states are fragile
Classical overhead: Circuit compilation and simulation costs

The Future

As quantum hardware improves, QML could revolutionize fields like drug discovery, financial modeling, optimization, and cryptography. The next decade will be crucial in determining the practical applications of quantum advantage in machine learning.