Evaluating analytic gradients on quantum hardware

Understand

An important application for near-term quantum computing lies in optimization tasks, with applications ranging from quantum chemistry and drug discovery to machine learning.

• In many settings --- most prominently in so-called parametrized or variational algorithms --- the objective function is a result of hybrid quantum-classical processing.
• To optimize the objective, it is useful to have access to exact gradients of quantum circuits with respect to gate parameters.
• This paper shows how gradients of expectation values of quantum measurements can be estimated using the same, or almost the same, architecture that executes the original circuit.