Julian Schuhmacher, Laura Boggia, et al.
Machine Learning: Science and Tech.
We develop a workflow to use current quantum computing hardware for solving quantum many-body problems, using the example of the fermionic Hubbard model. Concretely, we study a four-site Hubbard ring that exhibits a transition from a product state to an intrinsically interacting ground state as hopping amplitudes are changed. We locate this transition and solve for the ground-state energy with high quantitative accuracy using a variational quantum algorithm executed on an IBM quantum computer. Our results are enabled by a variational ansatz that takes full advantage of the maximal set of commuting Z2 symmetries of the problem and a Lanczos-inspired error mitigation algorithm. They are a benchmark on the way to exploiting near term quantum simulators for quantum many-body problems.
Julian Schuhmacher, Laura Boggia, et al.
Machine Learning: Science and Tech.
Takis Angelides, Pranay Naredi, et al.
npj Quantum Information
Marie-Anne Hervé du Penhoat, Alexandre Souchaud, et al.
Physical Chemistry Chemical Physics
Roberto Monni, Gloria Capano, et al.
PNAS