QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits

TL;DR

QAdaPrune is presented - an adaptive parameter pruning algorithm that automatically determines the threshold and then intelligently prunes the redundant and non-performing parameters and shows that the resulting sparse parameter sets yield quantum circuits that perform comparably to the unpruned quantum circuits and in some cases may enhance trainability of the circuits even if the original quantum circuit gets stuck in a barren plateau.

Abstract

In the present noisy intermediate scale quantum computing era, there is a critical need to devise methods for the efficient implementation of gate-based variational quantum circuits. This ensures that a range of proposed applications can be deployed on real quantum hardware. The efficiency of quantum circuit is desired both in the number of trainable gates and the depth of the overall circuit. The major concern of barren plateaus has made this need for efficiency even more acute. The problem of efficient quantum circuit realization has been extensively studied in the literature to reduce gate complexity and circuit depth. Another important approach is to design a method to reduce the \emph{parameter complexity} in a variational quantum circuit. Existing methods include hyperparameter-based parameter pruning which introduces an additional challenge of finding the best hyperparameters for different applications. In this paper, we present \emph{QAdaPrune} - an adaptive parameter pruning algorithm that automatically determines the threshold and then intelligently prunes the redundant and non-performing parameters. We show that the resulting sparse parameter sets yield quantum circuits that perform comparably to the unpruned quantum circuits and in some cases may enhance trainability of the circuits even if the original quantum circuit gets stuck in a barren plateau.\\ \noindent{\bf Reproducibility}: The source code and data are available at \url{https://github.com/aicaffeinelife/QAdaPrune.git}

Figures (7)

• Figure 1: Overview of a deployed VQC (top) and the QAdaPrune algorithm(bottom). The algorithm accepts a vector of parameters $\bm{\theta}_t$ and applies an adaptive pruning procedure to return pruned parameters. The procedure introduces minimal overhead in the training process of a VQC.
• Figure 2: A diagram of the QNN used in our experiments. The circuit is viewed left to right with data encoding being applied to the input before the variational circuit. A measurement is applied at the end of the circuit to produce the predictions. (Best viewed in color).
• Figure 3: Performance of VQE with UCCSD and customized ansatz with pruning and non-pruning setting. Left: Ground state energies obtained by different ansatz. Right: Difference in the ground state energy between pruned and unpruned cases.
• Figure 4: Non-UCCSD Ansatz initialized with random parameters used in our VQE experiments.
• Figure 5: The number of iterations taken to converge for the VQE optimization process for non pruned and pruned cases