64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
import os
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from multiprocessing import Queue
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import pennylane as qml
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from pennylane import numpy as np
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os.environ["OMP_NUM_THREADS"] = "16"
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def get_sctructure_from_xyz_path(path: str):
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return qml.qchem.read_structure("methane.xyz")
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def run_vqe(
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queue_callback: Queue,
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symbols,
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coordinates,
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active_electrons,
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active_orbitals,
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max_iterations,
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conv_tol,
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step_size,
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):
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molecule = qml.qchem.Molecule(symbols, coordinates, load_data=True)
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H, qubits = qml.qchem.molecular_hamiltonian(
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molecule, active_electrons=active_electrons, active_orbitals=active_orbitals
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)
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dev = qml.device("lightning.qubit", wires=qubits)
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singles, doubles = qml.qchem.excitations(active_electrons, qubits)
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params = np.array(np.zeros(len(singles) + len(doubles)), requires_grad=True)
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@qml.qnode(dev)
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def circuit(param, wires):
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# Map excitations to the wires the UCCSD circuit will act on
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s_wires, d_wires = qml.qchem.excitations_to_wires(singles, doubles)
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qml.UCCSD(
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param,
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wires,
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s_wires=s_wires,
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d_wires=d_wires,
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init_state=qml.qchem.hf_state(active_electrons, qubits),
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)
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return qml.expval(H)
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def cost_fn(param):
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return circuit(param, wires=range(qubits))
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opt = qml.GradientDescentOptimizer(stepsize=step_size)
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for n in range(max_iterations):
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# Take step
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params, prev_energy = opt.step_and_cost(cost_fn, params)
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energy = cost_fn(params)
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# Calculate difference between new and old energies
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conv = np.abs(energy - prev_energy)
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queue_callback.put([n, energy, params])
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if conv <= conv_tol:
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break
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