Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits
This work tackles optimization challenges in quantum machine learning for researchers, showing incremental progress by adapting a classical concept to a quantum setting.
The paper investigated whether the Lottery Ticket Hypothesis (LTH) applies to variational quantum circuits (VQCs) to address barren plateaus, finding that winning tickets retained 26.0% of parameters for weak LTH and achieved 100% accuracy with 45% of weights for strong LTH in a binary VQC.
Quantum computing is an emerging field in computer science that has seen considerable progress in recent years, especially in machine learning. By harnessing the principles of quantum physics, it can surpass the limitations of classical algorithms. However, variational quantum circuits (VQCs), which rely on adjustable parameters, often face the barren plateau phenomenon, hindering optimization. The Lottery Ticket Hypothesis (LTH) is a recent concept in classical machine learning that has led to notable improvements in parameter efficiency for neural networks. It states that within a large network, a smaller, more efficient subnetwork, or ''winning ticket,'' can achieve comparable performance, potentially circumventing plateau challenges. In this work, we investigate whether this idea can apply to VQCs. We show that the weak LTH holds for VQCs, revealing winning tickets that retain just 26.0\% of the original parameters. For the strong LTH, where a pruning mask is learned without any training, we discovered a winning ticket in a binary VQC, achieving 100\% accuracy with only 45\% of the weights. These findings indicate that LTH may mitigate barren plateaus by reducing parameter counts while preserving performance, thus enhancing the efficiency of VQCs in quantum machine learning tasks.