{"slug": "quantum-encoding-agents", "title": "Quantum Encoding Agents", "summary": "A paper submitted to arXiv on 25 Sep 2026 presents Quantum Encoding Agents, an open-source system that turns data-encoding selection in quantum machine learning into a natural-language interaction. Given a dataset and an optional task description, the system profiles the data, applies a hardware-aware policy using a gate-error threshold p* of approximately 10^-3, returns a copyable Qiskit circuit with a justification in Portuguese or English, and scores the quantum kernel by Kernel-Target Alignment. The system implements seven encoding families — amplitude, angle, dense angle, IQP, basis, data re-uploading, and custom feature map — and uses q* = max(2, ceil(D_2)) as a qubit budget derived from the correlation fractal dimension D_2 to avoid proposing angle and IQP maps at widths where the fidelity kernel has collapsed.", "body_md": "# Quantum Physics\n\n  [Submitted on 25 Sep 2026]\n\n# Title:Quantum Encoding Agents: A Natural Language Interface for Data Embedding Strategy Selection in Quantum Machine Learning\n\n[View PDF](https://arxiv.org/pdf/2609.31902)\n\n[HTML (experimental)](https://arxiv.org/html/2609.31902v1)\n\nAbstract:Selecting a data encoding is a central and poorly tooled decision in quantum machine learning. The feature map fixes the geometry of the Hilbert space, the expressibility of quantum kernels, and whether the circuit can run on near-term hardware. This paper presents Quantum Encoding Agents, an open-source system that turns encoding selection into a natural-language interaction. Given a dataset and an optional task description, it profiles the data, applies a hardware-aware policy using the gate-error threshold p* approximately 10^{-3}, returns a copyable Qiskit circuit with a justification in Portuguese or English, and scores the quantum kernel by Kernel-Target Alignment (KTA). When a numerical matrix is available, it estimates the correlation fractal dimension D_2 and selects original columns with FD-ASE, using q* = max(2, ceil(D_2)) as a qubit budget so that angle and IQP maps are not proposed at a width where the fidelity kernel has collapsed. The service implements seven encoding families: amplitude, angle, dense angle, IQP, basis, data re-uploading, and custom feature map. On benchmark datasets, KTA separates these encodings under realistic NISQ constraints.\n    \n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth 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.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/quantum-encoding-agents", "canonical_source": "https://arxiv.org/abs/2609.31902", "published_at": "2026-10-16 08:46:31+00:00", "updated_at": "2026-10-16 09:09:52.857590+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "ai-tools", "developer-tools"], "entities": ["arXiv", "Qiskit", "Quantum Encoding Agents", "Kernel-Target Alignment", "FD-ASE"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/quantum-encoding-agents", "markdown": "https://wpnews.pro/news/quantum-encoding-agents.md", "text": "https://wpnews.pro/news/quantum-encoding-agents.txt", "jsonld": "https://wpnews.pro/news/quantum-encoding-agents.jsonld"}}