Transfer Learning Analysis of Variational Quantum Circuits

Abstract

This work analyzes transfer learning of the Variational Quantum Circuit (VQC). Our framework begins with a pretrained VQC configured in one domain and calculates the transition of 1-parameter unitary subgroups required for a new domain. A formalism is established to investigate the adaptability and capability of a VQC under the analysis of loss bounds. Our theory observes knowledge transfer in VQCs and provides a heuristic interpretation for the mechanism. An analytical fine-tuning method is derived to attain the optimal transition for adaptations of similar domains.

arXiv commentPublished at ICASSP 2025

References in corpus (8)

• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum Circuit Learning
• Generalization in quantum machine learning from few training data
• Transfer learning in hybrid classical-quantum neural networks
• The Expressive Power of Parameterized Quantum Circuits
• Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
• Variational Quantum Reinforcement Learning via Evolutionary Optimization
• Applying QNLP to sentiment analysis in finance