Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithms

Understand

A broad class of hybrid quantum-classical algorithms known as "variational algorithms" have been proposed in the context of quantum simulation, machine learning, and combinatorial optimization as a means of potentially achieving a quantum speedup on a near-term quantum device for a problem of practical interest.

• Such algorithms use the quantum device only to prepare parameterized quantum states and make simple measurements.
• A classical controller uses the measurement results to perform an optimization of a classical function induced by a quantum observable which defines the problem.
• While most prior works have considered optimization strategies based on estimating the objective function and doing a derivative-free or finite-difference-based optimization, some recent proposals involve directly measuring observables corresponding to the gradient of the objective function.