> For the complete documentation index, see [llms.txt](https://docs.zama.org/concrete/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zama.org/concrete/get-started/terminology.md). # Terminology This document provides clear definitions of key concepts used in **Concrete** framework. **Computation graph** A data structure to represent a computation. It takes the form of a directed acyclic graph where nodes represent inputs, constants, or operations. **Tracing** A method that takes a Python function provided by the user and generates a corresponding computation graph. **Bounds** The minimum and the maximum value that each node in the computation graph can take. Bounds are used to determine the appropriate data type (for example, uint3 or int5) for each node before the computation graphs are converted to MLIR. **Concrete** simulates the graph with the inputs in the inputset to record the minimum and the maximum value for each node. **Circuit** The result of compilation. A circuit includes both client and server components. It has methods for various operations, such as printing and evaluation. **Table Lookup (TLU)** TLU stands for instructions in the form of y = T\[i]. In FHE, this operation is performed with Programmable Bootstrapping, which is the equivalent operation on encrypted values. To learn more about TLU, refer to the [Table Lookup basic](/concrete/operations/table_lookups.md) and tge [Table Lookup advanced](/concrete/explanations/advanced-features/table_lookups_advanced.md) section. **Programmable Bootstrapping (PBS)** PBS is equivalent to table lookup `y = T[i]` on encrypted values, which means that the inputs `i` and the outputs `y` are encrypted, but the table `T` is not encrypted. You can find a more detailed explanation in the [FHE Overview](/concrete/explanations/fhe_basics.md#noise-and-bootstrap). **TFHE** TFHE is a Fully Homomorphic Encryption (FHE) scheme that allows you to perform computations over encrypted data. For in-depth explanation of the TFHE scheme, read our blog post series [TFHE Deep Dive](https://www.zama.ai/post/tfhe-deep-dive-part-1). --- # 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.zama.org/concrete/get-started/terminology.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.