Variational Quantum Algorithms (VQAs)

VQAs are a class of hybrid quantum-classical algorithms that use a quantum computer to prepare an initial ansatz based on a parameterized quantum circuit.  A cost function is computed based on an estimate of the system energy usually related to the Hamiltonian.  This cost function is then passed to a classical computer and an optimization algorithm is run to minimize this cost function and generate a new set of parameters to be fed back to the quantum computer in an interactive loop until convergence.  A schematic of this process is shown below modeled after Cerezo et al. (2020).

VQA schematic modeled after Cerezo et al. (2020)

Applications of VQAs range from quantum chemistry, combinatorial optimization, and linear algebra and machine learning.  More information on several of the most popular categories can be found at the links below.

You can read more details about applications of the above algorithms here. Also, a recent video by one of the leading experts can be found by clicking on the link.