Hartree-Fock on a superconducting qubit quantum computer
Rubin, Nicholas; Babbush, Ryan; Google AI, and Collaborators (2020), Hartree-Fock on a superconducting qubit quantum computer, Dryad, Dataset, https://doi.org/10.5061/dryad.2jm63xsm2
The simulation of fermionic systems is among the most anticipated applications of quantum computing. Here, we performed several quantum simulations of chemistry with up to one dozen qubits, including modeling the isomerization mechanism of diazene. We also demonstrated error-mitigation strategies based on N-representability which dramatically improve the effective fidelity of our experiments. Our parameterized ansatz circuits realized the Givens rotation approach to non-interacting fermion evolution, which we variationally optimized to prepare the Hartree-Fock wavefunction. This ubiquitous algorithmic primitive is classically tractable to simulate, yet still generates highly entangled states over the computational basis, which allowed us to assess the performance of our hardware and establish a foundation for scaling up correlated quantum chemistry simulations.
This data was collected on the Sycamore processor from google used to achieve quantum supremacy. Data was processed using python code in https://github.com/quantumlib/ReCirq/tree/master/recirq/hfvqe (commit: 64c78758f5c4d139af3eeada42003485fbca17d1).