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Gradient-based optimization for quantum architecture search.
He, Zhimin; Wei, Jiachun; Chen, Chuangtao; Huang, Zhiming; Situ, Haozhen; Li, Lvzhou.
Afiliación
  • He Z; School of Electronic and Information Engineering, Foshan University, Foshan, 528000, China.
  • Wei J; School of Mathematics and Big Data, Foshan University, Foshan, 528000, China.
  • Chen C; Faculty of Innovation Engineering, Macau University of Science and Technology, Macao Special Administrative Region of China.
  • Huang Z; School of Economics and Management, Wuyi University, Jiangmen, 529020, China.
  • Situ H; College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China. Electronic address: situhaozhen@gmail.com.
  • Li L; Institute of Quantum Computing and Software, School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510006, China.
Neural Netw ; 179: 106508, 2024 Nov.
Article en En | MEDLINE | ID: mdl-39003982
ABSTRACT
Quantum Architecture Search (QAS) has shown significant promise in designing quantum circuits for Variational Quantum Algorithms (VQAs). However, existing QAS algorithms primarily explore circuit architectures within a discrete space, which is inherently inefficient. In this paper, we propose a Gradient-based Optimization for Quantum Architecture Search (GQAS), which leverages a circuit encoder, decoder, and predictor. Initially, the encoder embeds circuit architectures into a continuous latent representation. Subsequently, a predictor utilizes this continuous latent representation as input and outputs an estimated performance for the given architecture. The latent representation is then optimized through gradient descent within the continuous latent space based on the predicted performance. The optimized latent representation is finally mapped back to a discrete architecture via the decoder. To enhance the quality of the latent representation, we pre-train the encoder on a substantial dataset of circuit architectures using Self-Supervised Learning (SSL). Our simulation results on the Variational Quantum Eigensolver (VQE) indicate that our method outperforms the current Differentiable Quantum Architecture Search (DQAS).
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Teoría Cuántica / Algoritmos / Redes Neurales de la Computación Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Teoría Cuántica / Algoritmos / Redes Neurales de la Computación Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos