Loading [a11y]/accessibility-menu.js
Learning to Learn Variational Quantum Algorithm | IEEE Journals & Magazine | IEEE Xplore

Learning to Learn Variational Quantum Algorithm


Abstract:

Variational quantum algorithms (VQAs) use classical computers as the quantum outer loop optimizer and update the circuit parameters to obtain an approximate ground state....Show More

Abstract:

Variational quantum algorithms (VQAs) use classical computers as the quantum outer loop optimizer and update the circuit parameters to obtain an approximate ground state. In this article, we present a meta-learning variational quantum algorithm (meta-VQA) by recurrent unit, which uses a technique called “meta-learner.” Motivated by the hybrid quantum-classical algorithms, we train classical recurrent units to assist quantum computing, learning to find approximate optima in the parameter landscape. Here, aiming to reduce the sampling number more efficiently, we use the quantum stochastic gradient descent method and introduce the adaptive learning rate. Finally, we deploy on the TensorFlow Quantum processor within approximate quantum optimization for the Ising model and variational quantum eigensolver for molecular hydrogen (H2), lithium hydride (LiH), and helium hydride cation (HeH+). Our algorithm can be expanded to larger system sizes and problem instances, which have higher performance on near-term processors.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 34, Issue: 11, November 2023)
Page(s): 8430 - 8440
Date of Publication: 28 February 2022

ISSN Information:

PubMed ID: 35226607

Funding Agency:


Contact IEEE to Subscribe

References

References is not available for this document.