Understanding Quantum Algorithms 🌐
Quantum algorithms leverage the principles of superposition and entanglement to solve problems more efficiently than classical algorithms. Here we explore key algorithms that are shaping the future of computing.
Grover's Search Algorithm 🔍
Grover's algorithm searches an unsorted database of N items in O(√N) time, which is quadratically faster than classical search.
# Grover's algorithm example in Qiskit
from qiskit import QuantumCircuit, Aer, transpile, execute
qc = QuantumCircuit(2)
qc.h([0,1])
qc.cz(0,1)
qc.h([0,1])
qc.measure_all()
backend = Aer.get_backend('qasm_simulator')
result = execute(qc, backend, shots=1024).result()
counts = result.get_counts()
print(counts)
Shor's Factoring Algorithm 🔢
Shor's algorithm factors large integers exponentially faster than classical methods. It underpins quantum threat to RSA cryptography.
# Qiskit example (simplified)
from qiskit.algorithms import Shor
shor = Shor()
result = shor.factor(15)
print(result.factors)
Variational Quantum Algorithms ⚡
Variational algorithms combine quantum circuits with classical optimization, used for chemistry, machine learning, and combinatorial problems.
# Variational Quantum Eigensolver (VQE) in Qiskit
from qiskit import Aer
from qiskit.algorithms import VQE
from qiskit.circuit.library import TwoLocal
ansatz = TwoLocal(2, ['ry','rz'], 'cz', reps=3)
backend = Aer.get_backend('statevector_simulator')
vqe = VQE(ansatz, quantum_instance=backend)
# Placeholder for VQE computation
📖 Learn More
Explore more quantum algorithms like quantum teleportation, amplitude amplification, and QAOA.
Next: Simulations