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H₂ Molecule Homework Assignment

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Quantum Software Development Journey: From Theory to Application with Classiq - Part 3

  • Similarly to what we have done in class, in this exercise we will implement the VQE on H2 molecule.

  • This time instead of using the built-in methods and functions (such as Molecule and MoleculeProblem) to difne and solve the problem, you will be provided with a two qubits Hamiltonian.

Submission

  • Submit the completed Jupyter notebook and report via GitHub. Ensure all files are correctly named and organized.

  • Use the Typeform link provided in the submission folder to confirm your submission.

Additional Resources

Important Dates

  • Assignment Release: 22.5.2024

  • Submission Deadline: 3.6.2024 (7 A.M GMT+3)


Happy coding and good luck!

Part 1

Given the following Hamiltonian:

\[\hat{H} = -1.0523 \cdot (I \otimes I) + 0.3979 \cdot (I \otimes Z) - 0.3979 \cdot (Z \otimes I) - 0.0112 \cdot (Z \otimes Z) + 0.1809 \cdot (X \otimes X)\]

Complete the following code

# !pip install -U classiq
# import classiq
# classiq.authenticate()
from typing import List

from classiq import *

# TODO: Complete Hamiltonian
HAMILTONIAN = QConstant(
    "HAMILTONIAN",
    List[PauliTerm],
    [
        PauliTerm([Pauli.I, Pauli.I], -1.0523),
        PauliTerm([Pauli.I, Pauli.Z], 0.3979),
        PauliTerm([Pauli.Z, Pauli.I], -0.3979),
        PauliTerm([Pauli.Z, Pauli.Z], -0.0112),
        PauliTerm([Pauli.X, Pauli.X], 0.1809),
    ],
)
@qfunc
def main(q: Output[QArray[QBit]], angles: CArray[CReal, 3]) -> None:
    # TODO: Create an ansatz which allows each qubit to have
    # arbitrary rotation
    allocate(2, q)

    U(angles[0], angles[1], angles[2], 0, q[0])
    U(angles[0], angles[1], angles[2], 0, q[1])


@cfunc
def cmain() -> None:
    res = vqe(
        hamiltonian=HAMILTONIAN,
        maximize=False,
        initial_point=[],
        optimizer=Optimizer.COBYLA,
        max_iteration=1000,
        tolerance=0.001,
        step_size=0,
        skip_compute_variance=False,
        alpha_cvar=1.0,
    )
    save({"result": res})


# TODO: complete the line, use classical_execution_function
qmod = create_model(main, classical_execution_function=cmain)

qprog = synthesize(qmod)
execution = execute(qprog)
res = execution.result()
# execution.open_in_ide()
vqe_result = res[0].value
print(f"Optimal energy: {vqe_result.energy}")
print(f"Optimal parameters: {vqe_result.optimal_parameters}")
print(f"Eigenstate: {vqe_result.eigenstate}")
Optimal energy: -1.0619871093750002
Optimal parameters: {'angles_0': -3.2808520260537106, 'angles_1': 5.294346105087447, 'angles_2': -3.360401698136923}
Eigenstate: {'10': (0.07967217989988726+0j), '01': (0.08267972847076846+0j), '11': (0.9933863328282708+0j)}

Does it similar to the optimal energy we calculated in class? \ Does it similar to the total energy we calculated in class?

Part 2

Now, we want to have a more interesting ansatz in our main.
Add one line of code to the main function you created in Part 1 that creates entanglement between the two qubits.
Which gate should you use?

@qfunc
def main(q: Output[QArray[QBit]], angles: CArray[CReal, 3]) -> None:
    # TODO: Create an ansatz which allows each qubit to have
    # arbitrary rotation
    allocate(2, q)

    U(angles[0], angles[1], angles[2], 0, q[0])
    U(angles[0], angles[1], angles[2], 0, q[1])

    CX(q[0], q[1])


@cfunc
def cmain() -> None:
    res = vqe(
        HAMILTONIAN,  # TODO: complete the missing argument
        False,
        [],
        optimizer=Optimizer.COBYLA,
        max_iteration=1000,
        tolerance=0.001,
        step_size=0,
        skip_compute_variance=False,
        alpha_cvar=1.0,
    )
    save({"result": res})


# TODO: complete the line, use classical_execution_function
qmod = create_model(main, classical_execution_function=cmain)

qprog = synthesize(qmod)
execution = execute(qprog)
res = execution.result()
# execution.open_in_ide()
vqe_result = res[0].value
print(f"Optimal energy: {vqe_result.energy}")
print(f"Optimal parameters: {vqe_result.optimal_parameters}")
print(f"Eigenstate: {vqe_result.eigenstate}")
Optimal energy: -1.0626466796875
Optimal parameters: {'angles_0': 6.128115986875437, 'angles_1': -1.2908504032221542, 'angles_2': -0.0016859821535950559}
Eigenstate: {'11': (0.07654655446197431+0j), '10': (0.07967217989988726+0j), '00': (0.9938777433618282+0j)}

Does it similar to the optimal energy we calculated in class? \ Does it similar to the total energy we calculated in class? \ What can we learn about the provided form this result Hamiltonian?