Artificial Intelligence (AI) has undeniably been a revolutionary force in a vast array of fields. With the advent of Quantum Computing, the AI domain is bracing itself for another significant transformation. Quantum Machine Learning (QML), an exciting blend of Machine Learning (ML) and Quantum Physics, promises exponential speed-ups over traditional ML, opening new frontiers for data processing.
In this blog, we explore a basic introduction to Quantum Machine Learning. We will also implement a simple quantum circuit using the qiskit library, an open-source software development kit (SDK) for working with quantum computers at the level of pulses, circuits, and application modules.
Quantum Machine Learning uses quantum computers to accelerate the computation time of machine learning algorithms. Although today’s quantum computers are not yet fully capably of achieving this goal, the conception of QML algorithms is still crucial for future development.
Quantum algorithms can provide a substantial speed-up over classical algorithms, bringing new potential to the Machine Learning domain. As quantum computing progresses to practical usability, QML will pave the way for advanced data analysis, intricate pattern recognition and sophisticated predictive models.
qiskitTo experiment with Quantum Machine Learning, we first need to set the environment for Quantum Computing. We will be using qiskit, a Python library for creating, manipulating, and visualizing quantum circuits. Ensure you have Python (3.5 or later) installed to proceed.
Install qiskit library via pip:
!pip install qiskit
Using qiskit, we can construct a simple quantum circuit:
from qiskit import QuantumCircuit, transpile, assemble, Aer, execute from qiskit.visualization import plot_histogram, plot_bloch_vector from math import sqrt, pi # Create a Quantum Circuit with one qubit. qc = QuantumCircuit(1) # Apply a H-gate at circuit position 0. qc.h(0) qc.save_statevector() # Run the quantum circuit on a statevector simulator backend backend = Aer.get_backend('statevector_simulator') result = execute(qc, backend).result() # Get statevector result out_state = result.get_statevector() # Display circuit print(qc)
This is a basic example of a quantum circuit that applies a Hadamard gate, which puts a qubit (the quantum version of a bit) into a superposition state. We then run the circuit on a simulator provided by qiskit.
Incorporating quantum systems into machine learning constructs an exciting new paradigm for more complex data analysis. With the progress in quantum hardware, the potential application of Quantum Machine Learning is incredibly vast, promising transformative outputs. While we patiently await fully functioning quantum machines, it is both fascinating and crucial to conceptualize and devise the QML algorithms of the future.