Adaptive shot allocation for fast convergence in variational quantum algorithms

Understand

Variational Quantum Algorithms (VQAs) are a promising approach for practical applications like chemistry and materials science on near-term quantum computers as they typically reduce quantum resource requirements.

• However, in order to implement VQAs, an efficient classical optimization strategy is required.
• Here we present a new stochastic gradient descent method using an adaptive number of shots at each step, called the global Coupled Adaptive Number of Shots (gCANS) method, which improves on prior art in both the number of iterations as well as the number of shots required.
• These improvements reduce both the time and money required to run VQAs on current cloud platforms.