> For the complete documentation index, see [llms.txt](https://docs.quantaria.xyz/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.quantaria.xyz/deep-dives/dmrg-sweeps.md). # DMRG-style sweeps The density matrix renormalization group (DMRG) is a method from physics for finding low-energy states of matrix product states. Quantaria plans to test a DMRG-style sweep alongside gradient descent. This page explains the method and why it is a research question for neural networks. ## The method DMRG optimizes one core at a time. The other cores are held fixed, and the chosen core is set to the best solution of a local problem. The sweep then moves to the next core and repeats, back and forth across the chain. Each local step is cheap, and the sweep converges to a good solution for the whole chain. ## Relation to gradient descent Gradient descent updates all cores together, with a learning rate. DMRG updates one core exactly, given the rest. The two methods can be combined: sweeps can set local structure, and gradient steps can refine the whole model. ## Why it is open for neural layers * DMRG was designed for ground-state energy problems. A neural training loss is different, and the local problem may not be solvable exactly. * The sweep needs a stable centre (see [Canonical forms and sweeps](/deep-dives/canonical-forms-and-sweeps.md)). * It is not established that sweeps improve training on a real task. ## Quantaria's use Quantum-native training in the roadmap includes DMRG-style sweeps alongside gradient descent. It is research, not part of Hilbert-1. See [Roadmap](https://github.com/bhavy2908/dataai_docs/tree/main/overview/roadmap.md). ## Related * [Canonical forms and sweeps](/deep-dives/canonical-forms-and-sweeps.md) * [Rank-adaptive training](/deep-dives/rank-adaptive-training.md) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.quantaria.xyz/deep-dives/dmrg-sweeps.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.