The original variational quantum eigensolver(VQE)typically minimizes energy with hybrid quantum-classical optimization that aims to find the ground state.Here,we propose a VQE based on minimizing energy variance and c...The original variational quantum eigensolver(VQE)typically minimizes energy with hybrid quantum-classical optimization that aims to find the ground state.Here,we propose a VQE based on minimizing energy variance and call it the variance-VQE,which treats the ground state and excited states on the same footing,since an arbitrary eigenstate for a Hamiltonian should have zero energy variance.We demonstrate the properties of the variance-VQE for solving a set of excited states in quantum chemistry problems.Remarkably,we show that optimization of a combination of energy and variance may be more efficient to find low-energy excited states than those of minimizing energy or variance alone.We further reveal that the optimization can be boosted with stochastic gradient descent by Hamiltonian sampling,which uses only a few terms of the Hamiltonian and thus significantly reduces the quantum resource for evaluating variance and its gradients.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.12005065)the Guangdong Basic and Applied Basic Research Fund(Grant No.2021A1515010317)
文摘The original variational quantum eigensolver(VQE)typically minimizes energy with hybrid quantum-classical optimization that aims to find the ground state.Here,we propose a VQE based on minimizing energy variance and call it the variance-VQE,which treats the ground state and excited states on the same footing,since an arbitrary eigenstate for a Hamiltonian should have zero energy variance.We demonstrate the properties of the variance-VQE for solving a set of excited states in quantum chemistry problems.Remarkably,we show that optimization of a combination of energy and variance may be more efficient to find low-energy excited states than those of minimizing energy or variance alone.We further reveal that the optimization can be boosted with stochastic gradient descent by Hamiltonian sampling,which uses only a few terms of the Hamiltonian and thus significantly reduces the quantum resource for evaluating variance and its gradients.
