EAQAS: Embedding-Aware Quantum Architecture Search via cross-attention fusion and hierarchical representation learning
Year: 2026
Type: article
Abstract: Existing quantum architecture search (QAS) methods typically treat quantum data embedding and circuit structures as independent components, leading to inefficient search processes and limited performance. We propose EAQAS, an embedding-aware QAS framework that explicitly models the joint representation of embedding operations and quantum gates using a Hierarchical Variational Graph Autoencoder (H-VGAE) with bi-directional cross-attention fusion. The framework integrates RepCap-based filtering for search space pruning, dual-branch encoders for modality-specific representation learning, and Bayesian optimization in the learned latent space. On binary classification tasks using MNIST and WDBC, EAQAS achieves accuracies of 99.28% and 97.01%, respectively. RepCap filtering at moderate thresholds ( \(\tau =0.3\) –0.5) accelerates search by 1.43–1.74× while fully preserving accuracy. Ablation studies confirm that cross-modal reconstruction is the most critical component, with its removal causing a 5.16% average performance drop. This work demonstrates that embedding-aware representation learning can enhance automated quantum circuit design under NISQ-compatible constraints.
Source: EPJ Quantum Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,190
OpenAlex ID: W7130381730