Quantum Machine Learning (QML) is a new and exciting field that combines quantum physics and machine learning. It presents the idea of using quantum devices for computation to speed up machine learning tasks. In this blog post, we'll explore a basic example of Quantum Machine Learning with Python's Qiskit library.
Qiskit is an open-source framework from IBM that allows you to work with quantum computers at the level you want, from the application level right down to manually controlling individual quantum gates.
Before using Qiskit, we need to install it. We'll use pip, a Python package installer. Execute the below command in your Python environment:
!pip install qiskit
To start, let's build a simple quantum circuit with two qubits and two classical bits:
from qiskit import QuantumCircuit # Create a Quantum Circuit acting on a quantum register of two qubits qc = QuantumCircuit(2) # Add a H gate on qubit 0, putting this qubit in superposition. qc.h(0) # Add a CX (CNOT) gate on control qubit 0 and target qubit 1, putting the qubits in a Bell state. qc.cx(0, 1) # visualize the circuit print(qc)
Next, we will run this quantum circuit using Qiskit's Aer backend, which simulates the circuit and returns the quantum states as results.
from qiskit import execute, Aer # Use Aer's qasm_simulator backend_simulator = Aer.get_backend('qasm_simulator') # Execute the circuit on the qasm simulator, getting the result result = execute(qc, backend_simulator).result() # Print the result print(result.get_counts(qc))
This is a very basic introduction to Quantum Machine Learning with Qiskit library in Python. Although it may seem complex and theoretical now, Quantum Machine Learning promises to revolutionize AI by offering incredibly fast computational speeds. As we're still very early in the Quantum Computing era, there's a lot more to explore and understand.
This post only provides a high-level overview of Quantum Machine Learning. Advanced topics and real-life applications require a substantial amount of quantum physics, linear algebra, and computing knowledge.