Gate(s)-wise Optimization for Variational Quantum Eigensolvers

The variational quantum eigensolver (VQE) is a promising hybrid framework for solving chemistry and physics problems on noisy intermediate-scale quantum (NISQ) computers of the near future. VQE uses a NISQ device to prepare classically intractable parameterized quantum states. These states include ground states of chemistry and condensed matter models, as well as the solution to other optimization problems. A VQE method combines sampling from the NISQ device with a classical optimization routine to find target states. To optimize, most VQE algorithms use quantum circuits to measure gradients of a cost function in order to perform a gradient descent step. We provide and benchmark an alternative to gradient descent VQE methods with the potential for avoiding local minima and faster convergence. We demonstrate proof-of-concept results for local hamiltonians by optimizing one gate at a time.