Kod ulangan
Anggaran penggunaan: kurang dari 1 minit pada pemproses Heron (NOTA: Ini adalah anggaran sahaja. Masa jalan sebenar mungkin berbeza.)
Latar Belakangβ
Untuk membolehkan pembetulan ralat kuantum (QEC) masa nyata, anda perlu dapat mengawal aliran program kuantum secara dinamik semasa pelaksanaan supaya Gate kuantum boleh dikondisikan pada keputusan pengukuran. Tutorial ini menjalankan kod bit-flip, yang merupakan bentuk QEC yang sangat mudah. Ia menunjukkan litar kuantum dinamik yang boleh melindungi qubit yang dikodkan daripada ralat bit-flip tunggal, kemudian menilai prestasi kod bit-flip tersebut.
Anda boleh memanfaatkan qubit ancilla tambahan dan penjalingan untuk mengukur penstabil yang tidak mengubah maklumat kuantum yang dikodkan, sambil tetap memaklumkan anda tentang beberapa kelas ralat yang mungkin telah berlaku. Kod penstabil kuantum mengekodkan qubit logik ke dalam qubit fizikal. Kod penstabil memberi tumpuan secara kritikal pada pembetulan set ralat diskret dengan sokongan daripada kumpulan Pauli .
Untuk maklumat lanjut tentang QEC, rujuk Quantum Error Correction for Beginners.
Keperluanβ
Sebelum memulakan tutorial ini, pastikan anda telah memasang yang berikut:
- Qiskit SDK v2.0 atau lebih baru, dengan sokongan visualisasi
- Qiskit Runtime v0.40 atau lebih baru (
pip install qiskit-ibm-runtime)
Persediaanβ
# Added by doQumentation β required packages for this notebook
!pip install -q qiskit qiskit-ibm-runtime
# Qiskit imports
from qiskit import (
QuantumCircuit,
QuantumRegister,
ClassicalRegister,
)
# Qiskit Runtime
from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler
from qiskit_ibm_runtime.circuit import MidCircuitMeasure
service = QiskitRuntimeService()
Langkah 1. Petakan input klasik kepada masalah kuantumβ
Bina litar penstabil bit-flipβ
Kod bit-flip adalah antara contoh kod penstabil yang paling mudah. Ia melindungi keadaan daripada ralat bit-flip (X) tunggal pada mana-mana qubit pengekodan. Pertimbangkan tindakan ralat bit-flip , yang memetakan dan pada mana-mana qubit kita, maka kita ada . Kod ini memerlukan lima qubit: tiga digunakan untuk mengekodkan keadaan yang dilindungi, dan dua yang lain digunakan sebagai ancilla pengukuran penstabil.
# Choose the least busy backend that supports `measure_2`.
backend = service.least_busy(
filters=lambda b: "measure_2" in b.supported_instructions,
operational=True,
simulator=False,
dynamic_circuits=True,
)
qreg_data = QuantumRegister(3)
qreg_measure = QuantumRegister(2)
creg_data = ClassicalRegister(3, name="data")
creg_syndrome = ClassicalRegister(2, name="syndrome")
state_data = qreg_data[0]
ancillas_data = qreg_data[1:]
def build_qc():
"""Build a typical error correction circuit"""
return QuantumCircuit(qreg_data, qreg_measure, creg_data, creg_syndrome)
def initialize_qubits(circuit: QuantumCircuit):
"""Initialize qubit to |1>"""
circuit.x(qreg_data[0])
circuit.barrier(qreg_data)
return circuit
def encode_bit_flip(circuit, state, ancillas) -> QuantumCircuit:
"""Encode bit-flip. This is done by simply adding a cx"""
for ancilla in ancillas:
circuit.cx(state, ancilla)
circuit.barrier(state, *ancillas)
return circuit
def measure_syndrome_bit(circuit, qreg_data, qreg_measure, creg_measure):
"""
Measure the syndrome by measuring the parity.
We reset our ancilla qubits after measuring the stabilizer
so we can reuse them for repeated stabilizer measurements.
Because we have already observed the state of the qubit,
we can write the conditional reset protocol directly to
avoid another round of qubit measurement if we used
the `reset` instruction.
"""
circuit.cx(qreg_data[0], qreg_measure[0])
circuit.cx(qreg_data[1], qreg_measure[0])
circuit.cx(qreg_data[0], qreg_measure[1])
circuit.cx(qreg_data[2], qreg_measure[1])
circuit.barrier(*qreg_data, *qreg_measure)
circuit.append(MidCircuitMeasure(), [qreg_measure[0]], [creg_measure[0]])
circuit.append(MidCircuitMeasure(), [qreg_measure[1]], [creg_measure[1]])
with circuit.if_test((creg_measure[0], 1)):
circuit.x(qreg_measure[0])
with circuit.if_test((creg_measure[1], 1)):
circuit.x(qreg_measure[1])
circuit.barrier(*qreg_data, *qreg_measure)
return circuit
def apply_correction_bit(circuit, qreg_data, creg_syndrome):
"""We can detect where an error occurred and correct our state"""
with circuit.if_test((creg_syndrome, 3)):
circuit.x(qreg_data[0])
with circuit.if_test((creg_syndrome, 1)):
circuit.x(qreg_data[1])
with circuit.if_test((creg_syndrome, 2)):
circuit.x(qreg_data[2])
circuit.barrier(qreg_data)
return circuit
def apply_final_readout(circuit, qreg_data, creg_data):
"""Read out the final measurements"""
circuit.barrier(qreg_data)
circuit.measure(qreg_data, creg_data)
return circuit
def build_error_correction_sequence(apply_correction: bool) -> QuantumCircuit:
circuit = build_qc()
circuit = initialize_qubits(circuit)
circuit = encode_bit_flip(circuit, state_data, ancillas_data)
circuit = measure_syndrome_bit(
circuit, qreg_data, qreg_measure, creg_syndrome
)
if apply_correction:
circuit = apply_correction_bit(circuit, qreg_data, creg_syndrome)
circuit = apply_final_readout(circuit, qreg_data, creg_data)
return circuit
circuit = build_error_correction_sequence(apply_correction=True)
circuit.draw(output="mpl", style="iqp", cregbundle=False)
Langkah 2. Optimumkan masalah untuk pelaksanaan kuantumβ
Untuk mengurangkan jumlah masa pelaksanaan kerja, primitif Qiskit hanya menerima litar dan observable yang mematuhi arahan dan sambungan yang disokong oleh sistem sasaran (dirujuk sebagai litar dan observable ISA, iaitu instruction set architecture). Ketahui lebih lanjut tentang transpilasi.
Jana litar ISAβ
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuit = pm.run(circuit)
isa_circuit.draw("mpl", style="iqp", idle_wires=False)


no_correction_circuit = build_error_correction_sequence(
apply_correction=False
)
isa_no_correction_circuit = pm.run(no_correction_circuit)
Langkah 3. Jalankan menggunakan primitif Qiskitβ
Jalankan versi dengan pembetulan diterapkan dan satu lagi tanpa pembetulan.
sampler_no_correction = Sampler(backend)
job_no_correction = sampler_no_correction.run(
[isa_no_correction_circuit], shots=1000
)
result_no_correction = job_no_correction.result()[0]
sampler_with_correction = Sampler(backend)
job_with_correction = sampler_with_correction.run([isa_circuit], shots=1000)
result_with_correction = job_with_correction.result()[0]
print(f"Data (no correction):\n{result_no_correction.data.data.get_counts()}")
print(
f"Syndrome (no correction):\n{result_no_correction.data.syndrome.get_counts()}"
)
Data (no correction):
{'111': 878, '011': 42, '110': 35, '101': 40, '100': 1, '001': 2, '000': 2}
Syndrome (no correction):
{'00': 942, '10': 33, '01': 22, '11': 3}
print(f"Data (corrected):\n{result_with_correction.data.data.get_counts()}")
print(
f"Syndrome (corrected):\n{result_with_correction.data.syndrome.get_counts()}"
)
Data (corrected):
{'111': 889, '110': 25, '000': 11, '011': 45, '101': 17, '010': 10, '001': 2, '100': 1}
Syndrome (corrected):
{'00': 929, '01': 39, '10': 20, '11': 12}
Langkah 4. Proses pasca, kembalikan keputusan dalam format klasikβ
Anda boleh lihat bahawa kod bit-flip berjaya mengesan dan membetulkan banyak ralat, menghasilkan lebih sedikit ralat secara keseluruhan.
def decode_result(data_counts, syndrome_counts):
shots = sum(data_counts.values())
success_trials = data_counts.get("000", 0) + data_counts.get("111", 0)
failed_trials = shots - success_trials
error_correction_events = shots - syndrome_counts.get("00", 0)
print(
f"Bit flip errors were detected/corrected on {error_correction_events}/{shots} trials."
)
print(
f"A final parity error was detected on {failed_trials}/{shots} trials."
)
# non-corrected marginalized results
data_result = result_no_correction.data.data.get_counts()
marginalized_syndrome_result = result_no_correction.data.syndrome.get_counts()
print(
f"Completed bit code experiment data measurement counts (no correction): {data_result}"
)
print(
f"Completed bit code experiment syndrome measurement counts (no correction): {marginalized_syndrome_result}"
)
decode_result(data_result, marginalized_syndrome_result)
Completed bit code experiment data measurement counts (no correction): {'111': 878, '011': 42, '110': 35, '101': 40, '100': 1, '001': 2, '000': 2}
Completed bit code experiment syndrome measurement counts (no correction): {'00': 942, '10': 33, '01': 22, '11': 3}
Bit flip errors were detected/corrected on 58/1000 trials.
A final parity error was detected on 120/1000 trials.
# corrected marginalized results
corrected_data_result = result_with_correction.data.data.get_counts()
corrected_syndrome_result = result_with_correction.data.syndrome.get_counts()
print(
f"Completed bit code experiment data measurement counts (corrected): {corrected_data_result}"
)
print(
f"Completed bit code experiment syndrome measurement counts (corrected): {corrected_syndrome_result}"
)
decode_result(corrected_data_result, corrected_syndrome_result)
Completed bit code experiment data measurement counts (corrected): {'111': 889, '110': 25, '000': 11, '011': 45, '101': 17, '010': 10, '001': 2, '100': 1}
Completed bit code experiment syndrome measurement counts (corrected): {'00': 929, '01': 39, '10': 20, '11': 12}
Bit flip errors were detected/corrected on 71/1000 trials.
A final parity error was detected on 100/1000 trials.
Kaji selidik tutorialβ
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