Learning to learn with an evolutionary strategy applied to variational quantum algorithms

• URL: http://arxiv.org/abs/2310.17402v1
• Date: Thu, 26 Oct 2023 13:55:01 GMT
• Title: Learning to learn with an evolutionary strategy applied to variational quantum algorithms
• Authors: Lucas Friedrich, Jonas Maziero
• Abstract summary: Variational Quantum Algorithms (VQAs) employ quantum circuits parameterized by $U$, optimized using classical methods to minimize a cost function. In this article, we introduce a novel optimization approach named Learning to Learn with an Evolutionary Strategy'' (LLES) LLES treats optimization as a learning problem, utilizing recurrent neural networks to iteratively propose VQA parameters.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational Quantum Algorithms (VQAs) employ quantum circuits parameterized by $U$, optimized using classical methods to minimize a cost function. While VQAs have found broad applications, certain challenges persist. Notably, a significant computational burden arises during parameter optimization. The prevailing ``parameter shift rule'' mandates a double evaluation of the cost function for each parameter. In this article, we introduce a novel optimization approach named ``Learning to Learn with an Evolutionary Strategy'' (LLES). LLES unifies ``Learning to Learn'' and ``Evolutionary Strategy'' methods. ``Learning to Learn'' treats optimization as a learning problem, utilizing recurrent neural networks to iteratively propose VQA parameters. Conversely, ``Evolutionary Strategy'' employs gradient searches to estimate function gradients. Our optimization method is applied to two distinct tasks: determining the ground state of an Ising Hamiltonian and training a quantum neural network. Results underscore the efficacy of this novel approach. Additionally, we identify a key hyperparameter that significantly influences gradient estimation using the ``Evolutionary Strategy'' method.

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