LiePrune: Lie Group and Quantum Geometric Dual Representation for One-Shot Structured Pruning of Quantum Neural Networks
This addresses the problem of excessive parameters and hardware limitations in quantum machine learning for researchers and practitioners, representing a novel method rather than an incremental improvement.
The authors tackled the scalability issues of quantum neural networks (QNNs) by proposing LiePrune, a one-shot structured pruning framework that leverages Lie group and quantum geometric representations to detect redundancy and compress parameters. The result was over 10x compression with negligible or improved performance on tasks like quantum classification and chemistry.
Quantum neural networks (QNNs) and parameterized quantum circuits (PQCs) are key building blocks for near-term quantum machine learning. However, their scalability is constrained by excessive parameters, barren plateaus, and hardware limitations. We propose LiePrune, the first mathematically grounded one-shot structured pruning framework for QNNs that leverages Lie group structure and quantum geometric information. Each gate is jointly represented in a Lie group--Lie algebra dual space and a quantum geometric feature space, enabling principled redundancy detection and aggressive compression. Experiments on quantum classification (MNIST, FashionMNIST), quantum generative modeling (Bars-and-Stripes), and quantum chemistry (LiH VQE) show that LiePrune achieves over $10\times$ compression with negligible or even improved task performance, while providing provable guarantees on redundancy detection, functional approximation, and computational complexity.