Quantum Circuit Training with Growth-Based Architectures

TL;DR

This paper proposes growth-based strategies for training parameterized quantum circuits that adaptively increase complexity during training, leading to improved stability, convergence, and generalization in quantum machine learning tasks.

Contribution

It introduces three novel growth-based methods for PQC training that dynamically expand circuit depth, enhancing performance over fixed-depth approaches.

Findings

Outperform fixed-depth PQCs in regression and PDE tasks.

Achieve lower final losses and reduced variance.

Enhance stability and generalization in noisy environments.

Abstract

This study introduces growth-based training strategies that incrementally increase parameterized quantum circuit (PQC) depth during training, mitigating overfitting and managing model complexity dynamically. We develop three distinct methods: Block Growth, Sequential Feature Map Growth, and Interleave Feature Map Growth, which add reuploader blocks to PQCs adaptively, expanding the accessible frequency spectrum of the model in response to training needs. This approach enables PQCs to achieve more stable convergence and generalization, even in noisy settings. We evaluate our methods on regression tasks and the 2D Laplace equation, demonstrating that dynamic growth methods outperform traditional, fixed-depth approaches, achieving lower final losses and reduced variance between runs. These findings underscore the potential of growth-based PQCs for quantum scientific machine learning (QSciML) applications, where balancing expressivity and stability is essential.