Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing
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Quantum Physics arXiv:2601.06392 (quant-ph) [Submitted on 10 Jan 2026] Title:Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing Authors:Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Javier Tejedor, Ling Li, Min-Hsiu Hsieh View a PDF of the paper titled Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing, by Jun Qi and 5 other authors View PDF HTML (experimental) Abstract:We introduce CL-QAS, a continual quantum architecture search framework that mitigates the challenges of costly amplitude encoding and catastrophic forgetting in variational quantum circuits. The method uses Tensor-Train encoding to efficiently compress high-dimensional stochastic signals into low-rank quantum feature representations. A bi-loop learning strategy separates circuit parameter optimization from architecture exploration, while an Elastic Weight Consolidation regularization ensures stability across sequential tasks. We derive theoretical upper bounds on approximation, generalization, and robustness under quantum noise, demonstrating that CL-QAS achieves controllable expressivity, sample-efficient generalization, and smooth convergence without barren plateaus. Empirical evaluations on electrocardiogram (ECG)-based signal classification and financial time-series forecasting confirm substantial improvements in accuracy, balanced accuracy, F1 score, and reward. CL-QAS maintains strong forward and backward transfer and exhibits bounded degradation under depolarizing and readout noise, highlighting its potential for adaptive, noise-resilient quantum learning on near-term devices. Comments: Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG); Signal Processing (eess.SP) Cite as: arXiv:2601.06392 [quant-ph] (or arXiv:2601.06392v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.06392 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jun Qi [view email] [v1] Sat, 10 Jan 2026 02:36:03 UTC (739 KB) Full-text links: Access Paper: View a PDF of the paper titled Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing, by Jun Qi and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-01 Change to browse by: cs cs.LG eess eess.SP 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?) Links to Code Toggle Papers with Code (What is Papers with Code?) 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?)