Bridge the Gap between Classical and Quantum Neural Networks with Residual Connections

Quantum Physics [Submitted on 17 Apr 2026] Bridge the Gap between Classical and Quantum Neural Networks with Residual Connections Junxu Li We introduce a Hybrid Quantum Residual Network (HQRN) and establish an exact functional correspondence between its state evolution and the dynamics of classical networks with residual connections. When inputs are restricted to the computational basis, the HQRN reduces to its classical analog, enabling the direct translation of optimized classical weights into quantum unitary operations, effectively inheriting the landscape benefits of classical optimization. Conversely, when processing general mixed states, the HQRN leverages off-diagonal quantum correlations to resolve features inaccessible to its classical analog. We validate this framework through digit recognition and bipartite entanglement classification. Notably, HQRN achieves high classification accuracy even for adversarial separable states that mimic the marginal measurement statistics of entangled pairs. Our results bridge the gap between classical and quantum residual learning, paving a scalable pathway for deep quantum architectures. Comments: 17 Pages, 8 Figures Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.15626 [quant-ph]   (or arXiv:2604.15626v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2604.15626 Focus to learn more Submission history From: Junxu Li [view email] [v1] Fri, 17 Apr 2026 02:07:10 UTC (2,171 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-04 References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)