Model selection in hybrid quantum neural networks with applications to quantum transformer architectures
Quantum machine learning models generally lack principled design guidelines, often requiring full resource-intensive training across numerous choices of encodings, quantum circuit designs and initialization strategies to find effective configuration. To address this challenge, we develope the Quantum Bias-Expressivity Toolbox ($\texttt{QBET}$), a framework for evaluating quantum, classical, and hybrid transformer architectures. In this toolbox, we introduce lean metrics for Simplicity Bias ($\texttt{SB}$) and Expressivity ($\texttt{EXP}$), for comparing across various models, and extend the analysis of $\texttt{SB}$ to generative and multiclass-classification tasks. We show that $\texttt{...
| Authors | Harsh Wadhwa, Rahul Bhowmick, Naipunnya Raj et al. |
|---|---|
| Primary category | quant-ph |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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