Exploring the Effects of Entanglement on Quantum Machine Learning of Pathogen Epitope-Receptor Binding

Quantum Physics arXiv:2606.28655 (quant-ph) [Submitted on 27 Jun 2026] Title:Exploring the Effects of Entanglement on Quantum Machine Learning of Pathogen Epitope-Receptor Binding Authors:Aspen Erlandsson Brisebois, Luis Pablo Gonzalez Dominguez, Shivansi Prajapati, Zahed Khatooni, Heather L. Wilson, Connor Burbridge, Brook Byrns, Sureesh Tikoo, Christophe Pere, Steven Rayan, Gordon Broderick View a PDF of the paper titled Exploring the Effects of Entanglement on Quantum Machine Learning of Pathogen Epitope-Receptor Binding, by Aspen Erlandsson Brisebois and 10 other authors View PDF Abstract:Parameterized quantum circuits (PQCs) provide a flexible substrate for hybrid quantum machine learning (QML), but their practical value on Noisy Intermediate-Scale Quantum (NISQ) devices remains an empirical question, especially because training depth and scale can introduce optimization challenges such as barren plateaus. Here we study how the number and topology of two-qubit entangling gates in the feature-map stage influence a fixed hybrid QNN workflow for classifying strong versus weak epitope-receptor binding in Porcine Reproductive and Respiratory Syndrome (PRRS) vaccine design. The dataset consists of docking-derived binding affinities for N=80 9-mer epitopes, labeled as Strong or Weak binding, and partitioned into training, validation, and test subsets using a 40:30:30 split. We compare a classical CNN benchmark with a hybrid Embedding-QNN architecture under four feature-map configurations: a non-entangling Z feature map, an all-to-all high-entanglement ZZ feature map, and two interleaved nearest-neighbour entanglement patterns of low and high depth. Among the configurations tested, the high-entanglement ZZ feature map is seen to provide the strongest evidence of reduced training-set overfit, with a lower training area under the accuracy curve (AUAC) and the highest test/training AUAC ratio, while preserving competitive test-set accuracy. These results do not establish a general QML advantage, but they suggest that feature-map entanglement topology is a meaningful design variable for sparse biological screening tasks and warrants further evaluation with additional metrics, larger datasets, and noise-aware or hardware-based experiments. Comments: Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG); Biomolecules (q-bio.BM) Cite as: arXiv:2606.28655 [quant-ph] (or arXiv:2606.28655v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2606.28655 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Steven Rayan [view email] [v1] Sat, 27 Jun 2026 00:11:37 UTC (5,347 KB) Full-text links: Access Paper: View a PDF of the paper titled Exploring the Effects of Entanglement on Quantum Machine Learning of Pathogen Epitope-Receptor Binding, by Aspen Erlandsson Brisebois and 10 other authorsView PDF view license Current browse context: quant-ph new | recent | 2026-06 Change to browse by: cs cs.LG q-bio q-bio.BM References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: 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 Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)