gganbu marketplace

a_i | m_l | py | PyTorch

Quantum Machine Learning in 2025: The Power of QML, VQCs, and QSVMs with Qiskit

Quantum Machine Learning (QML) represents the intersection of two of the most transformative technologies—quantum computing and machine learning. In 2025, QML is evolving to tackle problems that are intractable for classical systems, offering quantum speed-ups for various computational tasks, such as classification, optimization, and clustering. Central to QML are Variational Quantum Circuits (VQC) and Quantum Support Vector Machines (QSVMs), which leverage quantum advantages in machine learning models. This article takes a deep dive into these concepts and provides real code examples using Qiskit, the industry-leading quantum computing framework.

Quantum Machine Learning (QML): Revolutionizing Machine Learning in 2025

Quantum Machine Learning (QML) refers to the integration of quantum computing techniques into machine learning workflows. QML takes advantage of quantum parallelism, quantum entanglement, and superposition to process information in ways classical systems cannot. In 2025, QML has demonstrated promising results, especially in areas like pattern recognition, neural network optimization, and clustering. While classical machine learning models require enormous computational power, quantum systems have the potential to provide exponential speed-ups.

QML algorithms typically rely on the principles of hybrid quantum-classical systems, where quantum circuits handle parts of the problem that benefit from quantum computation (e.g., feature encoding and kernel methods), while classical components solve the more traditional aspects (e.g., optimization).

Variational Quantum Circuits (VQCs)

Variational Quantum Circuits (VQCs) are at the heart of many quantum machine learning models. A VQC is a parameterized quantum circuit where parameters are optimized using classical techniques (like gradient descent). VQCs are particularly powerful in scenarios involving optimization, classification, and data encoding.

The workflow of VQCs typically involves:

  1. Feature Encoding: Classical data is encoded into quantum states.
  2. Quantum Circuit Processing: A quantum circuit with trainable parameters processes the data.
  3. Measurement & Classical Optimization: Results from quantum measurements are used to update parameters through a classical optimization routine.

In 2025, VQCs are being widely applied to quantum neural networks, reinforcement learning, and Quantum Natural Language Processing (QNLP).

VQC Code Example (Qiskit)

Let’s explore a VQC applied to a classification problem using Qiskit:

import numpy as np
from qiskit import QuantumCircuit, Aer, transpile, execute
from qiskit.circuit import Parameter
from qiskit_machine_learning.algorithms import VQC
from qiskit_machine_learning.circuit.library import TwoLayerQNN
from qiskit_machine_learning.utils import algorithm_globals
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from qiskit_machine_learning.neural_networks import CircuitQNN

# Generate and preprocess data
X, y = make_classification(n_samples=100, n_features=2, n_classes=2, random_state=42)
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Define the feature map (encoding data into quantum states)
qc = QuantumCircuit(2)
parameters = [Parameter(f"θ{i}") for i in range(2)]
qc.h([0, 1])  # Apply Hadamard gate
qc.ry(parameters[0], 0)
qc.ry(parameters[1], 1)

# Create a quantum neural network
quantum_neural_network = TwoLayerQNN(num_qubits=2, feature_map=qc)

# Define and train the VQC
vqc = VQC(quantum_neural_network, optimizer='SPSA')
vqc.fit(X_train, y_train)

# Evaluate on test data
accuracy = vqc.score(X_test, y_test)
print(f"VQC Test Accuracy: {accuracy}")

Explanation:

  • Feature Encoding: The quantum circuit encodes the classical input data into quantum states using parameterized gates like ry().
  • Two-Layer QNN: The quantum neural network processes the encoded data using a variational approach, where the circuit parameters are optimized based on the cost function.
  • Optimization: Classical optimization techniques (in this case, the SPSA optimizer) are used to train the VQC, optimizing the circuit parameters based on quantum measurement results.

In 2025, VQCs are being applied to tasks such as image recognition, financial modeling, and material science simulations—tasks that benefit from their ability to handle complex, high-dimensional data with quantum efficiency.

Quantum Support Vector Machines (QSVMs)

Quantum Support Vector Machines (QSVMs) are a quantum analog of classical Support Vector Machines (SVMs), used for classification tasks. QSVMs utilize quantum kernels, which leverage quantum states to compute inner products in high-dimensional Hilbert spaces. The key advantage of QSVMs is their ability to implicitly process data in a quantum-enhanced feature space, making them particularly suitable for complex classification tasks where classical SVMs may struggle.

The concept behind QSVMs is to use a quantum feature map to map classical data into quantum states. By applying quantum operations, QSVMs can separate data that is otherwise inseparable by classical SVMs, leading to more accurate predictions.

QSVM Code Example (Qiskit)

Let’s implement a QSVM using Qiskit in 2025:

from qiskit import Aer
from qiskit_machine_learning.kernels import QuantumKernel
from qiskit_machine_learning.algorithms import QSVM
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from qiskit.circuit.library import ZZFeatureMap

# Load classical dataset
data = load_breast_cancer()
X = data['data']
y = data['target']

# Preprocess the data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2)

# Define a feature map for the QSVM
feature_map = ZZFeatureMap(feature_dimension=30, reps=2)

# Create a quantum kernel
quantum_kernel = QuantumKernel(feature_map=feature_map, quantum_instance=Aer.get_backend('statevector_simulator'))

# Train QSVM
qsvm = QSVM(quantum_kernel=quantum_kernel)
qsvm.fit(X_train, y_train)

# Evaluate on test data
accuracy = qsvm.score(X_test, y_test)
print(f"QSVM Test Accuracy: {accuracy}")

Explanation:

  • Quantum Feature Map: The ZZFeatureMap encodes classical data into quantum states, capturing non-linear relationships between features in a quantum-enhanced space.
  • Quantum Kernel: This kernel computes the inner products between quantum states, leveraging the quantum parallelism to accelerate computations.
  • QSVM Classifier: The QSVM uses the quantum kernel to classify data in a high-dimensional quantum feature space.

In 2025, QSVMs are being applied to industries that deal with large datasets and require sophisticated pattern recognition, such as finance, healthcare diagnostics, and climate modeling.

Quantum Natural Language Processing (QNLP) with VQC and QSVMs

One of the most exciting areas of development in 2025 is Quantum Natural Language Processing (QNLP), where VQC and QSVM models are being used to process and classify text data in high-dimensional quantum feature spaces. By encoding language data into quantum circuits, QNLP models can perform tasks like text classification, sentiment analysis, and semantic parsing more efficiently than classical NLP models.

In the context of Natural Language Understanding (NLU), QNLP models can process and classify complex syntactic and semantic structures of human language using quantum-enhanced kernels, opening the door for future advancements in quantum AI-driven chatbots and automated customer service.

The Future of QML in 2025: A Quantum Leap for Machine Learning

The year 2025 marks a significant step forward in QML, with tools like Qiskit enabling researchers and developers to push the boundaries of what’s possible. VQCs and QSVMs are just two examples of how quantum algorithms are being harnessed for real-world machine learning applications.

Key trends to watch in QML include:

  • Hybrid Quantum-Classical Architectures: Combining quantum circuits with classical optimization methods will continue to be a dominant trend.
  • Quantum Neural Networks (QNNs): With more qubits and better error correction, QNNs will outperform classical neural networks in select domains.
  • Quantum Kernel Methods: Quantum kernels will expand to more complex and higher-dimensional datasets, offering even greater classification accuracy.

By embracing these quantum techniques today, companies and researchers are positioning themselves for breakthroughs in AI, data science, and optimization that were unimaginable just a few years ago. Whether you’re building quantum-enhanced machine learning models for finance, healthcare, or technology, Qiskit in 2025 offers a powerful framework to explore this new quantum frontier.

In conclusion, QML is not just a future concept—it’s happening now. With Variational Quantum Circuits and Quantum Support Vector Machines, we’re entering an era where quantum systems will increasingly outperform classical approaches, bringing tangible benefits across multiple industries.