How to use VQE in Jasp

In How to think in Jasp we learned how Jasp allows to future proof Qrisp code for practically relevant problems. For variational quantum algorithms like QAOA and VQE, hybrid quantum-classical workflows can be seamlessly compliled, optimized and executed.

We again use the example of calculating the ground state energy of the \(H_2\) molecule with VQE. Running this example in Jasp is as easy as wrapping the code in a main function:

from qrisp import *
from qrisp.vqe.problems.electronic_structure import *
from pyscf import gto

def main():

    mol = gto.M(
        atom = '''H 0 0 0; H 0 0 0.74''',
        basis = 'sto-3g')

    vqe = electronic_structure_problem(mol)

    energy = vqe.run(lambda : QuantumFloat(4), depth=1, max_iter=100, optimizer="SPSA")

    return energy

The jaspify method allows for running Jasp-traceable functions using the integrated Qrisp simulator. For hybrid algorithms like QAOA and VQE that rely on calculating expectation values based on sampling, the terminal_sampling feature significantly speeds up the simulation: samples are drawn from the state vector instead of performing repeated simulation and measurement of the quantum circuits.

jaspify(main, terminal_sampling=True)()
#Yields: Array(-1.84467526, dtype=float64)

You can also create the Jaspr object and compile to QIR using Catalyst.

jaspr = make_jaspr(main)()
qir_str = jaspr.to_qir()