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  • Letter
  • Open Access

Training variational quantum algorithms with random gate activation

Shuo Liu1, Shi-Xin Zhang2,*, Shao-Kai Jian3,†, and Hong Yao1,‡

  • 1Institute for Advanced Study, Tsinghua University, Beijing 100084, China
  • 2Tencent Quantum Laboratory, Tencent, Shenzhen, Guangdong 518057, China
  • 3Department of Physics & Engineering Physics, Tulane University, New Orleans, Louisiana 70118, USA

  • *shixinzhang@tencent.com
  • †sjian@tulane.edu
  • ‡yaohong@tsinghua.edu.cn
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Phys. Rev. Research 5, L032040 – Published 18 September, 2023

DOI: https://doi.org/10.1103/PhysRevResearch.5.L032040

Abstract

Variational quantum algorithms (VQAs) hold great potential for near-term applications and are promising to achieve quantum advantage in practical tasks. However, VQAs suffer from severe barren plateau problems and have a significant probability of being trapped in local minima. In this Research Letter, we propose a training algorithm with random quantum gate activation for VQAs to efficiently address these two issues. This algorithm processes effectively many fewer training parameters than the conventional plain optimization strategy, which efficiently mitigates barren plateaus with the same expressive capability. Additionally, by randomly adding two-qubit gates to the circuit ansatz, the optimization trajectories can escape from local minima and reach the global minimum more frequently due to more sources of randomness. In real quantum experiments, the training algorithm can also reduce the quantum computational resources required and be more quantum noise resilient. We apply our training algorithm to solve variational quantum simulation problems for ground states and present convincing results that showcase the advantages of our strategy, where better performance is achieved by the combination of mitigating barren plateaus, escaping from local minima, and reducing the effect of quantum noise.

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Phys. Rev. Research 5, L032040– Published 18 September, 2023
  • Received 29 March 2023
  • Revised 30 May 2023
  • Accepted 30 August 2023
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