https://doi.org/10.1140/epjqt/s40507-026-00478-y
Research
EAQAS: Embedding-Aware Quantum Architecture Search via cross-attention fusion and hierarchical representation learning
1
Department of Electronic Engineering and Computer Science, School of Science and Technology, Hong Kong Metropolitan University, Kowloon, Hong Kong, China
2
Department of Health and Physical Education, The Education University of Hong Kong, Hong Kong, China
3
Research and Development, OptiMicro Technologies Inc, Ontario, Canada
a
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b
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Received:
6
January
2026
Accepted:
4
February
2026
Published online:
18
February
2026
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 (
–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.
Key words: Quantum machine learning / Quantum architecture search / Cross-attention fusion / Hierarchical representation learning / Quantum circuit optimization
© The Author(s) 2026
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