IBM Qiskit

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 diagrams

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]}")

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]

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())

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}")