Learning capability of parametrized quantum circuits

• URL: http://arxiv.org/abs/2209.10345v2
• Date: Wed, 13 Mar 2024 21:54:26 GMT
• Title: Learning capability of parametrized quantum circuits
• Authors: Dirk Heimann, Gunnar Schönhoff, Elie Mounzer, Hans Hohenfeld, Frank Kirchner,
• Abstract summary: Variational quantum algorithms (VQAs) and their applications in the field of quantum machine learning through parametrized quantum circuits (PQCs) are thought to be one major way of leveraging noisy intermediate-scale quantum computing devices. In this paper, we build upon the work by Schuld et al. and compare popular ans"atze for PQCs through the new measure of learning capability. We also examine dissipative quantum neural networks (dQNN) as introduced by Beer et al. and propose a data re-upload structure for dQNNs to increase their learning capability.
• Score: 2.51657752676152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational quantum algorithms (VQAs) and their applications in the field of quantum machine learning through parametrized quantum circuits (PQCs) are thought to be one major way of leveraging noisy intermediate-scale quantum computing devices. However, differences in the performance of certain VQA architectures are often unclear since established best practices as well as detailed studies are missing. In this paper, we build upon the work by Schuld et al. and Vidal et al. and compare popular ans\"atze for PQCs through the new measure of learning capability. We also examine dissipative quantum neural networks (dQNN) as introduced by Beer et al. and propose a data re-upload structure for dQNNs to increase their learning capability. Comparing the results for the different PQC architectures, we can provide guidelines for designing efficient PQCs.

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