quntumcomputertest/hello.ipynb

In [1]:

import qiskit

In [2]:

qiskit.__version__

Out [2]:

'1.4.2'

In [ ]:

from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService(channel="ibm_quantum", token="e047c8fd398ecad4aacdd2d4086476bce18bcb9974535a0cd00bfe410f34630acaf1f06f6c3df7bfc35723a27d6ea25f0246defe92c3ebfb82209d66101ffbcd")

In [5]:

backend = service.backend(name="ibm_kyiv")

In [ ]:

backend.num_qubits # erwarteter output: 127 Qubits verfügbar

127

In [ ]:

from qiskit import QuantumCircuit

qc = QuantumCircuit(2) #2 Qubits

qc.h(0) #hadamard Gatter auf Qubit 0 anwenden (superposition)
qc.cx(0, 1) #verschränkter Zustand

qc.draw(output="mpl") #das quantum circuit malen

Matplotlib is building the font cache; this may take a moment.

In [ ]:

from qiskit.quantum_info import Pauli

zz = Pauli("ZZ") #pauli matrix z x 2 (wirkt auf 2 qubit)
zi = Pauli("ZI") #pauli matrix z kombiniert mit identity matrix
iz = Pauli("IZ")
xx = Pauli("XX")
xi = Pauli("XI")
ix = Pauli("IX")

obs = [zz, zi, iz, xx, xi, ix]

In [13]:

from qiskit_aer.primitives import Estimator

estimator = Estimator()

job = estimator.run([qc] * len(obs), obs)

job.result()

Out [13]:

EstimatorResult(values=array([1.        , 0.03515625, 0.03515625, 1.        , 0.06054688,
       0.06054688]), metadata=[{'shots': 1024, 'variance': 0.0, 'simulator_metadata': [{'num_bind_params': 1, 'runtime_parameter_bind': False, 'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.00038575, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'active_input_qubits': [0, 1], 'device': 'CPU', 'time_taken': 0.000848583, 'measure_sampling': True, 'num_clbits': 2, 'max_memory_mb': 8192, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'stabilizer', 'required_memory_mb': 0, 'fusion': {'enabled': False}}]}, {'shots': 1024, 'variance': 0.9987640380859375, 'simulator_metadata': [{'num_bind_params': 1, 'runtime_parameter_bind': False, 'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.00038575, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'active_input_qubits': [0, 1], 'device': 'CPU', 'time_taken': 0.000848583, 'measure_sampling': True, 'num_clbits': 2, 'max_memory_mb': 8192, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'stabilizer', 'required_memory_mb': 0, 'fusion': {'enabled': False}}]}, {'shots': 1024, 'variance': 0.9987640380859375, 'simulator_metadata': [{'num_bind_params': 1, 'runtime_parameter_bind': False, 'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.00038575, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'active_input_qubits': [0, 1], 'device': 'CPU', 'time_taken': 0.000848583, 'measure_sampling': True, 'num_clbits': 2, 'max_memory_mb': 8192, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'stabilizer', 'required_memory_mb': 0, 'fusion': {'enabled': False}}]}, {'shots': 1024, 'variance': 0.0, 'simulator_metadata': [{'num_bind_params': 1, 'runtime_parameter_bind': False, 'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.001163167, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'active_input_qubits': [0, 1], 'device': 'CPU', 'time_taken': 0.001712458, 'measure_sampling': True, 'num_clbits': 2, 'max_memory_mb': 8192, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'stabilizer', 'required_memory_mb': 0, 'fusion': {'enabled': False}}]}, {'shots': 1024, 'variance': 0.9963340759277344, 'simulator_metadata': [{'num_bind_params': 1, 'runtime_parameter_bind': False, 'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.001163167, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'active_input_qubits': [0, 1], 'device': 'CPU', 'time_taken': 0.001712458, 'measure_sampling': True, 'num_clbits': 2, 'max_memory_mb': 8192, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'stabilizer', 'required_memory_mb': 0, 'fusion': {'enabled': False}}]}, {'shots': 1024, 'variance': 0.9963340759277344, 'simulator_metadata': [{'num_bind_params': 1, 'runtime_parameter_bind': False, 'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.001163167, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'active_input_qubits': [0, 1], 'device': 'CPU', 'time_taken': 0.001712458, 'measure_sampling': True, 'num_clbits': 2, 'max_memory_mb': 8192, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'stabilizer', 'required_memory_mb': 0, 'fusion': {'enabled': False}}]}])

In [14]:

import matplotlib.pyplot as pl

data = ["ZZ", "ZI", "IZ", "XX", "XI", "IX"]
values = job.result().values

pl.plot(data, values, '-o')
pl.xlabel("obs")
pl.ylabel("erwartet")
pl.show()