Transfer Learning Analysis of Variational Quantum Circuits
This work addresses the challenge of adapting quantum machine learning models to new tasks, but it appears incremental as it builds on existing VQC methods without introducing a new paradigm.
The paper tackles the problem of analyzing transfer learning for Variational Quantum Circuits (VQCs) by developing a framework to calculate parameter transitions and derive loss bounds, resulting in an analytical fine-tuning method for optimal adaptation to new domains.
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.