Simulating large-size quantum spin chains on cloud-based superconducting
  quantum computers
Quantum computers have the potential to efficiently simulate large-scale
quantum systems for which classical approaches are bound to fail. Even though
several existing quantum devices now feature total qubit numbers of more than
one hundred, their applicability remains plagued by the presence of noise and
errors. Thus, the degree to which large quantum systems can successfully be
simulated on these devices remains unclear. Here, we report on cloud
simulations performed on several of IBM's superconducting quantum computers to
simulate ground states of spin chains having a wide range of system sizes up to
one hundred and two qubits. We find that the ground-state energies extracted
from realizations across different quantum computers and system sizes reach the
expected values to within errors that are small (i.e. on the percent level),
including the inference of the energy density in the thermodynamic limit from
these values. We achieve this accuracy through a combination of
physics-motivated variational Ansatzes, and efficient, scalable
energy-measurement and error-mitigation protocols, including the use of a
reference state in the zero-noise extrapolation. By using a 102-qubit system,
we have been able to successfully apply up to 3186 CNOT gates in a single
circuit when performing gate-error mitigation. Our accurate, error-mitigated
results for random parameters in the Ansatz states suggest that a standalone
hybrid quantum-classical variational approach for large-scale XXZ models is
feasible.
Hongye Yu Yusheng Zhao Tzu-Chieh Wei