Exponentiation and Hamiltonian Simulation

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This tutorial demonstrates how to use the Classiq platform exponentiation function to solve Hamiltonian simulation problems, thereby demonstrating the strength of the Classiq exponentiation module.

Chemical Simulation

Chemical simulation is one of the most exciting applications for quantum computers. When precise simulations of electron-electron interactions are necessary, it is sometimes possible to use a classical computer, but classical computers struggle to simulate more complex molecular interactions. It is best to simulate these particle interactions at the quantum level, and an excellent way to do this is with a quantum computer. The ability to accurately simulate molecular interactions will have extensive applications. When used for drug discovery, it will allow for the rapid development of vaccines and new cures for diseases. In materials research, we can hope to discover materials with higher strength-to-weight ratios and environmentally friendly building materials.

The H₂O Hamiltonian Simulation Problem

Generate a circuit that approximates the unitary

e^{-iH}

where

H

is the qubit Hamiltonian of a H₂O (water) molecule. The H₂O Hamiltonian is composed of 551 Pauli strings on twelve qubits.

!pip install -qq "classiq[chemistry]"

from openfermion.chem import MolecularData
from openfermionpyscf import run_pyscf

from classiq import *
from classiq.applications.chemistry.mapping import FermionToQubitMapper
from classiq.applications.chemistry.op_utils import qubit_op_to_qmod
from classiq.applications.chemistry.problems import FermionHamiltonianProblem

molecule_H2O_geometry = [
    ("O", (0.0, 0.0, 0.0)),
    ("H", (0, 0.586, 0.757)),
    ("H", (0, 0.586, -0.757)),
]
molecule = MolecularData(molecule_H2O_geometry, "sto-3g", 1, 0)

molecule = run_pyscf(molecule)

problem = FermionHamiltonianProblem.from_molecule(molecule, first_active_index=1)
mapper = FermionToQubitMapper()
hamiltonian = qubit_op_to_qmod(mapper.map(problem.fermion_hamiltonian))

@qfunc
def main(state: Output[QArray]) -> None:
    allocate(hamiltonian.num_qubits, state)
    suzuki_trotter(
        hamiltonian,
        evolution_coefficient=1,
        order=1,
        repetitions=1,
        qbv=state,
    )


preferences = Preferences(
    custom_hardware_settings=CustomHardwareSettings(basis_gates=["cx", "u"])
)


qprog = synthesize(main, preferences=preferences)

print(
    f"Classiq's exponentiation depth is {get_transpiled_circuit_metrics(qprog).depth}"
)
print(
    f"Classiq's exponentiation CX-count is {get_transpiled_circuit_metrics(qprog).count_ops['cx']}"
)
show(qprog)

Output:


Classiq's exponentiation depth is 1471

Output:

Classiq's exponentiation CX-count is 1550
  Quantum program link: https://platform.classiq.io/circuit/3HojwUh21Q8w0sOY01NcqmwFDS9
  

These impressive results can be compared to the naive exponentiation modules often found in the literature, see comprehensive comparison in the Hamiltonian Evolution notebook.

Conclusion

Classiq packages the domain expertise of dozens of scientists and quantum software engineers into the software platform. The result: a system that can automatically generate efficient quantum circuits for complex problems, making it faster and easier than ever to solve real-life problems with quantum computing. When the circuits are of manageable size, Classiq creates solutions that are on par with the best manually created circuits. When the circuits are larger than those a human can reasonably create, Classiq allows you to progress farther because of its powerful capabilities.

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Exponentiation and Hamiltonian Simulation

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