  Finding accurate ground state energy of a many-body system has been a major
challenge in quantum chemistry. The integration of classic and quantum
computers has shed new light on resolving this outstanding problem. Here we
propose QiankunNet-VQE, a transformer based language models enforced with
quantum computing to learn and generate quantum states. It has been implemented
using up to 12 qubits and attaining an accuracy level competitive with
state-of-the-art classical methods. By leveraging both quantum and classical
resources, this scheme overcomes the limitations of variational quantum
eigensolver(VQE) without the need for cumbersome error mitigation. Moreover,
QiankunNet-VQE provides a different route to achieve a practical quantum
advantage for solving many-electron Schr\"odinger equation without requiring
extremely precise preparation and measurement of the ground-state wavefunction
on quantum computer.
