arXiv ID: 2210.06484v1
Title: Fast gradient estimation for variational quantum algorithms
Language: English
Authors: Bittel, Lennart 
Watty, Jens 
Kliesch, Martin 
Keywords: Quantum Physics; Quantum Physics
Issue Date: 12-Oct-2022
Source: arXiv:2210.06484v1 (2022-10-12)
Abstract (english): 
Many optimization methods for training variational quantum algorithms are based on estimating gradients of the cost function. Due to the statistical nature of quantum measurements, this estimation requires many circuit evaluations, which is a crucial bottleneck of the whole approach. We propose a new gradient estimation method to mitigate this measurement challenge and reduce the required measurement rounds. Within a Bayesian framework and based on the generalized parameter shift rule, we use prior information about the circuit to find an estimation strategy that minimizes expected statistical and systematic errors simultaneously. We demonstrate that this approach can significantly outperform traditional gradient estimation methods, reducing the required measurement rounds by up to an order of magnitude for a common QAOA setup. Our analysis also shows that an estimation via finite differences can outperform the parameter shift rule in terms of gradient accuracy for small and moderate measurement budgets.
Document Type: Preprint
Project: Effiziente Materialsimulation auf NISQ-Quantencomputern - Effizientes Auslesen von hybriden Quantencomputern 
Verifizierung und Charakterisierung von Quantentechnologie 
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