Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits

Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits

Abstract:

Quantum computing is an emerging field in computer science that has made significant progress in recent years, including in the area of machine learning. Through the principles of quantum physics, it offers the possibility of overcoming the limitations of classical algorithms. However, variational quantum circuits (VQCs), a specific type of quantum circuits utilizing varying parameters, face a significant challenge from the barren plateau phenomenon, which can hinder the optimization process in certain cases. The Lottery Ticket Hypothesis (LTH) is a recent concept in classical machine learning that has led to notable improvements in neural networks. In this thesis, we investigate whether it can be applied to VQCs. The LTH claims that within a large neural network, there exists a smaller, more efficient subnetwork (a “winning ticket”) that can achieve comparable performance. Applying this approach to VQCs could help reduce the impact of the barren plateau problem. The results of this thesis show that the weak LTH can be applied to VQCs, with winning tickets discovered that retain as little as 26.0% of the original weights. For the strong LTH, where a pruning mask is learned without any training, we found a winning ticket for a binary VQC, performing at 100% accuracy with 45% remaining weights. This shows that the strong LTH is also applicable to VQCs. These findings provide initial evidence that the LTH may be a valuable tool for improving the efficiency and performance of VQCs in quantum machine learning tasks.

Author:

Leonhard Klingert

Advisors:

Michael Kölle, Julian Schönberger, Claudia Linnhoff-Popien

Student Thesis | Published November 2024 | Copyright © QAR-Lab
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