LoopyLuci Quantum Computing with Qiskit
Implementing quantum algorithms with IBM Qiskit — from qubits and quantum gates through Grover's search, Shor's algorithm, and variational quantum eigensolvers.
When to Use
- Learning quantum computing concepts
- Implementing quantum algorithms on simulators or real hardware
- Exploring quantum advantage for specific problems
- Building hybrid quantum-classical algorithms
Quantum Computing Basics
from qiskit import QuantumCircuit, transpile
from qiskit_aer import AerSimulator
from qiskit.visualization import plot_histogram
# Bell state: entanglement
qc = QuantumCircuit(2, 2)
qc.h(0) # Hadamard gate on qubit 0
qc.cx(0, 1) # CNOT gate (control=0, target=1)
qc.measure([0, 1], [0, 1])
# Run on simulator
simulator = AerSimulator()
compiled = transpile(qc, simulator)
result = simulator.run(compiled, shots=1024).result()
counts = result.get_counts()
# Expected: {'00': ~512, '11': ~512} — entanglement!
# Grover's search: unsorted database search O(√N)
def grover_search(n_qubits: int, target: str):
qc = QuantumCircuit(n_qubits, n_qubits)
qc.h(range(n_qubits)) # Superposition
# Oracle + diffusion (repeated √N times)
# ...
return qc
Verification Checklist
- Quantum circuit designed for target algorithm
- Simulator tests pass (statevector or AerSimulator)
- Circuit depth and gate count optimized
- Error mitigation considered for real hardware
- Results statistically significant (enough shots)
- Classical hybrid integration working (if VQE/QAOA)
- Algorithm complexity understood (quantum speedup)