Qiskit Chemistry迁移至Qiskit Nature：Hamiltonian与TransformationType替代咨询

核心迁移对应关系 #

• qiskit.chemistry.core.Hamiltonian：无需直接导入，通过Qiskit Nature的问题类自动生成哈密顿量
• qiskit.chemistry.core.TransformationType：替换为qiskit_nature.units.DistanceUnit
• qiskit.chemistry.drivers.PySCFDriver：替换为qiskit_nature.second_q.drivers.PySCFDriver
• qiskit.aqua模块（VQE、SLSQP等）：迁移至qiskit.algorithms及子模块

修改后的完整代码 #

import numpy as np
from qiskit import Aer
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import SLSQP
from qiskit.circuit.library import TwoLocal
from qiskit_nature.second_q.drivers import PySCFDriver
from qiskit_nature.units import DistanceUnit
from qiskit_nature.second_q.problems import ElectronicStructureProblem
from qiskit_nature.second_q.mappers import ParityMapper, QubitConverter

# Set the number of qubits and optimization parameters
depth = 3

# Set up the molecule and driver
molecule = 'H .0 .0 -{0}; H .0 .0 {0}'
distance = 0.74
# 替换原TransformationType.ANGSTROM为DistanceUnit.ANGSTROM
driver = PySCFDriver(
    atom=molecule.format(distance/2),
    unit=DistanceUnit.ANGSTROM,
    charge=0,
    spin=0,
    basis='sto3g'
)
# 生成电子结构问题，替代原qmolecule.get_molecular_hamiltonian()
problem = ElectronicStructureProblem(driver)
# 转换为量子比特哈密顿量
converter = QubitConverter(ParityMapper(), two_qubit_reduction=True)
hamiltonian = problem.second_q_ops()['ElectronicEnergy']
qubit_hamiltonian = converter.convert(hamiltonian)
n_qubits = qubit_hamiltonian.num_qubits

# Define the ansatz circuit
ansatz = TwoLocal(n_qubits, ['ry', 'rz'], 'cz', reps=depth)

# Define the optimizer
optimizer = SLSQP(maxiter=1000)

# Define the VQE algorithm（移除原QuantumInstance，直接用backend）
backend = Aer.get_backend('statevector_simulator')
vqe = VQE(ansatz, optimizer, quantum_instance=backend)

# Run the VQE algorithm
result = vqe.compute_minimum_eigenvalue(qubit_hamiltonian)

# 获取并打印基态能量
electronic_result = problem.interpret(result)
print('Ground state energy: ', electronic_result.total_energies[0].real)

关键修改说明 #

1. 驱动与问题定义：用ElectronicStructureProblem封装驱动，替代原qmolecule的哈密顿量生成逻辑
2. 哈密顿量转换：通过QubitConverter将第二量子化哈密顿量转为量子比特哈密顿量，支持Parity映射及两比特约简（对应原Chemistry的默认行为）
3. 模块迁移：qiskit.aqua下的VQE、SLSQP等已迁移至qiskit.algorithms，无需再导入aqua_globals或QuantumInstance（直接传入backend即可）
4. 结果解析：用problem.interpret()解析VQE结果，获取包含核排斥能的总能量（原代码默认包含该能量，迁移后需显式解析）

内容的提问来源于stack exchange，提问作者Kenson Wesley