Quantum Machine Learning: An Intriguing Intersection

Quantum computing and machine learning, two of the most fascinating disciplines of modern science, are joining forces to create Quantum Machine Learning (QML). QML algorithms use underlying quantum mechanical properties to enhance computational capabilities, providing potentially groundbreaking solutions to traditional machine learning problems.

Understanding Quantum Computing

Traditional computers process bits, which can either be set to 0 or 1. Quantum computers, however, liberate us from binary computation by operating on 'qubits.' A qubit is in a state of superposition, meaning it can be both 0 and 1 simultaneously. This property allows quantum computers to process a tremendous amount of information compared to classical computers.

Quantum Machine Learning Algorithm: VQE

The Variational Quantum Eigensolver (VQE) is a prominent QML algorithm. It's designed to find the lowest eigenvalue (ground state energy) of a given Hamiltonian (total energy operator) of a quantum system.

Let's see an implementation powered by the Qiskit library in Python.

from qiskit import Aer
from qiskit.aqua import QuantumInstance, aqua_globals
from qiskit.aqua.algorithms import VQE
from qiskit.aqua.components.variational_forms import RYRZ
from qiskit.aqua.components.optimizers import SPSA
from qiskit.chemistry.components.initial_states import HartreeFock
from qiskit.chemistry.components.variational_forms import UCCSD
from qiskit.chemistry.drivers import PySCFDriver

aqua_globals.random_seed = 99

driver = PySCFDriver(atom='Li .0 .0 .0; H .0 .0 1.6', unit='Angstrom', charge=0, spin=0, basis='sto3g')
qmolecule = driver.run() 
operator =  FermionicOperator(h1=qmolecule.one_body_integrals, h2=qmolecule.two_body_integrals)
map_type = 'JORDAN_WIGNER'
qubitOp = operator.mapping(map_type)

initial_state = HartreeFock(qubitOp.num_qubits, operator.molecule_info['num_orbitals'], 
                            operator.molecule_info['num_particles'], qubit_mapping=map_type)

var_form = UCCSD(num_orbitals=operator.molecule_info['num_orbitals'], 
                 num_particles=operator.molecule_info['num_particles'], 
                 initial_state=initial_state, qubit_mapping=map_type)

spsa = SPSA(max_trials=200)
vqe_algorithm = VQE(qubitOp, var_form, spsa)
backend = Aer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend, seed_simulator=aqua_globals.random_seed, 
                                   seed_transpiler=aqua_globals.random_seed)
result = vqe_algorithm.run(quantum_instance)

This code simulates the molecule 'LiH,' and the VQE algorithm is used to find its ground state energy. The resulting value approximates the exact result.

Stay Tuned

Keep an eye on this fascinating field. When quantum computers become more accessible, quantum machine learning algorithms can revolutionize how we solve complex computations. Until our next deep-dive into the sea of knowledge, happy learning!