A novel approach to reduce derivative costs in variational quantum algorithms

We present a detailed numerical study of an alternative approach, named quantum non-demolition measurement (QNDM) (Solinas et al 2023 Eur. Phys. J. D 77 76), to efficiently estimate the gradients or the Hessians of a quantum observable. This is a key step and a resource-demanding task when we want to minimize the cost function associated with a quantum observable. In our detailed analysis, we account for all the resources needed to implement the QNDM approach with a fixed accuracy and compare them to the current state-of-the-art method (Mari et al 2021 Phys. Rev. A 103 012405; Schuld et al 2019 Phys. Rev. A 99 032331; Cerezo et al 2021 Nat. Rev. Phys. 3 625). We find that the QNDM approach is more efficient, i.e. it needs fewer resources, in evaluating the derivatives of a cost function. These advantages are already clear in small dimensional systems and are likely to increase for practical implementations and more realistic situations. A significant outcome of our study is the implementation of the QNDM method in Python, provided in the supplementary material (Caletti and Minuto 2024 https://github.com/ simonecaletti/qndm-gradient). Given that most variational quantum algorithms (VQA) can be formulated within this framework, our results can have significant implications in quantum optimization algorithms and make the QNDM approach a valuable alternative to implement VQA on near-term quantum computers.