Qu'est-ce que Qiskit ?
Qiskit est le SDK d'informatique quantique open source d'IBM. Il fournit des outils pour composer, visualiser, optimiser et exécuter des circuits quantiques sur les simulateurs IBM Quantum et le vrai matériel quantique. L'offre gratuite inclut l'accès à plusieurs QPU sans coût au-delà du temps d'attente.
📦
v1.x
Version actuelle
🖥️
30+ qubits
Simulateur Aer
⚛️
127 qubits
QPU réel gratuit
🆓
Gratuit
Compte ouvert
Installation
terminal
pip install qiskit # Core pip install qiskit-aer # Local simulator pip install qiskit-ibm-runtime # IBM Quantum cloud access pip install qiskit[visualization] # Optional: circuit diagramsConfigurer l'accès gratuit à IBM Quantum
- 1Rendez-vous sur quantum.ibm.com et cliquez sur « Sign in » → « Create an IBMid » (gratuit).
- 2Après la connexion, allez dans votre profil (en haut à droite) → « Manage account » → « API token ».
- 3Copiez le jeton d'API et collez-le dans l'appel save_account() ci-dessous.
- 4Exécutez le script de configuration une fois. Les identifiants sont enregistrés dans ~/.qiskit/qiskit-ibm.json.
setup_credentials.py
from qiskit_ibm_runtime import QiskitRuntimeService # Save your IBM Quantum token (only needed once) QiskitRuntimeService.save_account( channel="ibm_quantum", token="YOUR_IBM_QUANTUM_TOKEN_HERE", overwrite=True ) # Verify the connection service = QiskitRuntimeService(channel="ibm_quantum") backends = service.backends() print(f"Available backends: {[b.name for b in backends]}")Exécution sur le simulateur local gratuit
Qiskit Aer fournit un simulateur local haute performance. Aucun compte requis — exécutez un nombre illimité de circuits sur votre machine.
local_sim.py
from qiskit import QuantumCircuit from qiskit_aer import AerSimulator from qiskit.visualization import plot_histogram # Build a Bell state circuit qc = QuantumCircuit(2, 2) qc.h(0) qc.cx(0, 1) qc.measure([0, 1], [0, 1]) # Run on local Aer simulator (free, unlimited) sim = AerSimulator() job = sim.run(qc, shots=4096) result = job.result() counts = result.get_counts() print(counts) # {'00': ~2048, '11': ~2048} # Statevector simulation (no measurement noise) from qiskit.quantum_info import Statevector sv = Statevector.from_instruction(qc.remove_final_measurements(inplace=False)) print(sv) # [0.707+0j, 0, 0, 0.707+0j]Exécution sur du vrai matériel QPU gratuit
real_hardware.py
from qiskit import QuantumCircuit, transpile from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler service = QiskitRuntimeService(channel="ibm_quantum") # Find the least-busy free QPU backend = service.least_busy( operational=True, simulator=False, min_num_qubits=2 ) print(f"Running on: {backend.name} ({backend.num_qubits} qubits)") # Build circuit qc = QuantumCircuit(2, 2) qc.h(0) qc.cx(0, 1) qc.measure_all() # Transpile for the specific backend qc_t = transpile(qc, backend, optimization_level=3) # Submit job using SamplerV2 (modern Qiskit Runtime API) with Sampler(mode=backend) as sampler: job = sampler.run([qc_t], shots=1024) result = job.result() print(result[0].data.meas.get_counts())Solveur propre quantique variationnel (VQE)
vqe_example.py
from qiskit.circuit.library import TwoLocal from qiskit.quantum_info import SparsePauliOp from qiskit_ibm_runtime import QiskitRuntimeService, Session from qiskit_ibm_runtime import EstimatorV2 as Estimator from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager import numpy as np # Define a simple Hamiltonian (H2 molecule) hamiltonian = SparsePauliOp.from_list([ ("ZZ", -1.0523732), ("IZ", 0.3979374), ("ZI", -0.3979374), ("XX", 0.1809312), ("YY", 0.1809312), ]) # Ansatz circuit ansatz = TwoLocal(2, ['ry', 'rz'], 'cx', reps=2) init_params = np.zeros(ansatz.num_parameters) service = QiskitRuntimeService(channel="ibm_quantum") backend = service.least_busy(operational=True, simulator=False) pm = generate_preset_pass_manager(backend=backend, optimization_level=1) ansatz_isa = pm.run(ansatz) hamiltonian_isa = hamiltonian.apply_layout(ansatz_isa.layout) with Session(backend=backend) as session: estimator = Estimator(mode=session) job = estimator.run([(ansatz_isa, hamiltonian_isa, init_params)]) print(f"Energy estimate: {job.result()[0].data.evs}")💡
Also available via HLQuantum
Want to run the same circuit on multiple backends without rewriting your code? HLQuantum abstracts this SDK (and 5 others) behind a single unified API.
python
import hlquantum as hlq qc = hlq.Circuit(2) qc.h(0).cx(0, 1).measure_all() # One line to switch between any backend result = hlq.run(qc, shots=1024) # auto-detect result = hlq.run(qc, shots=1024, backend="qiskit") # explicit