Authors:
Jonas Stein
1
;
2
;
Tobias Rohe
1
;
Francesco Nappi
1
;
Julian Hager
1
;
David Bucher
2
;
Maximilian Zorn
1
;
Michael Kölle
1
and
Claudia Linnhoff-Popien
1
Affiliations:
1
Mobile and Distributed Systems Group, LMU Munich, Germany
;
2
Aqarios GmbH, Germany
Keyword(s):
Quantum Computing, Variational Quantum Circuits, Circuit Optimization, Variational Quantum Eigensolver.
Abstract:
Variational Quantum Algorithms are one of the most promising candidates to yield the first industrially relevant quantum advantage. Being capable of arbitrary function approximation, they are often referred to as Quantum Neural Networks (QNNs) when being used in analog settings as classical Artificial Neural Networks (ANNs). Similar to the early stages of classical machine learning, known schemes for efficient architectures of these networks are scarce. Exploring beyond existing design patterns, we propose a reduced-width circuit ansatz design, which is motivated by recent results gained in the analysis of dropout regularization in QNNs. More precisely, this exploits the insight, that the gates of overparameterized QNNs can be pruned substantially until their expressibility decreases. The results of our case study show, that the proposed design pattern can significantly reduce training time while maintaining the same result quality as the standard "full-width" design in the presence
of noise. We thus argue, that quantum architecture search should not blindly follow the classical overparameterization trend.
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