.png){kind=icon}
Return
Training circuit-based quantum classifiers through memetic algorithms
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2023.04.008.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Among the ready-to-implement quantum algorithms, Variational Quantum Circuits (VQCs) play a key role in several applications, including machine learning. Their strength lies in the use of a parameterized quantum circuit that is trained by means of an optimization algorithm run on a classical computer. In such a scenario, there is a strong need to design appropriate classical optimization schemes that deal efficiently with VQCs and pave the way for quantum advantage in machine learning. Among possible optimization schemes, those based on evolutionary computation are finding increasing interest, given the unconventional and nonanalytical nature of the problem to be solved. This paper proposes to apply memetic algorithms to train VQCs used as quantum classifiers and shows the benefits of exploiting this evolutionary optimization technique through a comparative experimental session.(c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Quantum machine learning
Variational quantum circuits
Quantum classifiers
Memetic algorithms
Optimization
Journal
IF:
3.3
Papers:
8.0K
Citations:
1.6W
Organization
Cited Papers
.png){kind=icon}
ShareSaveMemetic algorithms for mapping p-body interacting systems in effective quantum 2-body Hamiltonians
PREAI
ShareSaveShareSaveShareSaveTheoretical error performance analysis for variational quantum circuit based functional regression
OAAI
OAAIShareSaveShareSaveShareSave
View more