Quantum Machine Learning Example

This is a simple example of Quantum Machine Learning using Python and the Qiskit library.

Quantum Machine Learning Overview

Quantum Machine Learning combines concepts from quantum computing and machine learning to potentially outperform classical algorithms for certain tasks. Quantum algorithms, such as Quantum Support Vector Machines (QSVM), aim to take advantage of quantum parallelism and superposition to speed up computations.

Key concepts of Quantum Machine Learning:

• Quantum Bits (Qubits): Quantum counterparts of classical bits that can exist in superposition.
• Quantum Parallelism: The ability to process multiple inputs simultaneously using superposition.
• Quantum Gates: Operations applied to qubits, forming quantum circuits.
• Quantum Algorithms: Algorithms designed to run on quantum computers or simulators.

Python Source Code:

# Import necessary libraries
from qiskit import Aer, QuantumCircuit, transpile, assemble
from qiskit.ml.datasets import ad_hoc_data
from qiskit.aqua import QuantumInstance
from qiskit.aqua.algorithms import QSVM
from qiskit.aqua.components.multiclass_extensions import AllPairs

# Load a synthetic dataset for binary classification
feature_dim = 2
training_dataset_size = 20
testing_dataset_size = 10
random_seed = 42

sample_total, training_input, test_input, class_labels = ad_hoc_data(training_size=training_dataset_size,
                                                                    test_size=testing_dataset_size,
                                                                    n=feature_dim,
                                                                    gap=0.3,
                                                                    plot_data=False)

# Create a quantum support vector machine (QSVM) instance
backend = Aer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=random_seed, seed_transpiler=random_seed)

qsvm = QSVM(AllPairs(), training_input, test_input, None, quantum_instance)

# Train the QSVM
result = qsvm.run()

# Print the results
print(f'Testing success ratio: {result["testing_accuracy"]}')

# Get the quantum circuit underlying the QSVM
circuit = qsvm.construct_circuit(training_input)
print(circuit)

Explanation:

• Import Libraries: Import necessary Python libraries, including Qiskit for quantum computing.
• Load Synthetic Dataset: Load a synthetic dataset for binary classification using Qiskit's ad_hoc_data function.
• Create Quantum Support Vector Machine (QSVM): Create a QSVM instance for binary classification using the AllPairs multiclass extension.
• Train QSVM: Train the QSVM on the provided training dataset.
• Print Results: Print the testing success ratio obtained by the QSVM.
• Get Quantum Circuit: Retrieve the quantum circuit underlying the QSVM for inspection.