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3 Branches of Computation Theory

Updated: Oct 17, 2022

Computation theory is the study of the fundamental limits of computing. It is a relatively new field that only emerged in the 1940s with the development of digital computers. Since then, it has grown into a thriving field of research with many different branches. In this blog post, we will explore three of the most important branches of computation theory: complexity theory, cryptography, and quantum computing We will briefly define each area of study and discuss some of the most important research findings in each field.


An algorithm is a set of instructions for carrying out a task or calculation. It is usually written in a specific order so that it can be followed by a computer. The branch of computation theory that deals with algorithms are called algorithmics or algorithmic analysis. Algorithmics is the study of algorithms and their properties. Algorithms are used to solve problems in many different areas, such as mathematics, sciences, engineering, and business. They are also used in everyday tasks such as sorting data, searching for information, and routing traffic. There are three main types of algorithms: sequential, parallel, and distributed. Sequential algorithms are those that are carried out in a single thread or process. Parallel algorithms are those that can be executed in multiple threads or processes at the same time. Distributed algorithms are those that can be executed on multiple machines at the same time. Algorithms can be categorized by their efficiency as well. Some algorithms are more efficient than others and can be completed in less time or with fewer resources. There are also randomized algorithms, which use randomness to improve their efficiency or performance.

Complexity Theory

Complexity theory is the branch of computation theory that deals with the study of algorithms and computational complexity. It is concerned with the resources required to run an algorithm, such as time and space, and with the efficiency of algorithms.

Information Theory

Information theory is a branch of applied mathematics and electrical engineering that deals with the transmission, processing, storage, and retrieval of information. It has been fundamental to the development of computer networks, wireless communication, digital data storage, and Data compression. Information theory was developed by Claude Shannon in 1948 as a way to quantify information about big win casinos.

The P vs NP Problem

The P vs NP problem is one of the most fundamental and important problems in computation theory. It asks whether every problem that can be solved by a polynomial-time algorithm (P) can also be solved by an algorithm that runs in polynomial time, but with some extra computational resources (NP). This is a famously difficult problem to solve, and there is currently no consensus on whether P equals NP or not. However, there are many researchers who believe that P does not equal NP, and that this would have profound implications for the field of computation.

Turing Machines

A Turing machine is a theoretical machine that is capable of performing any task that can be done by a computer. The machine is named after Alan Turing, who first proposed it in 1936. The key difference between a Turing machine and a regular computer is that a Turing machine can be in one of an infinite number of states, whereas a regular computer can only be in one of a finite number of states. This allows the Turing machine to perform more complex computations than a regular computer. Turing machines are used in the field of computational complexity theory, which studies the resources required to perform various computation tasks. They are also used in the study of algorithms, which are sets of instructions for performing specific tasks.


In computation theory, the term "undecidability" refers to the inability of a given computational model to solve a problem in a finite amount of time. This is often due to the fact that the problem itself is unsolvable, or that the computational model is too weak to solve it.


There are three branches of computation theory: automata theory, complexity theory, and quantum computing. Each branch explores a different aspect of computing and helps us to better understand the limits of what computers can do. Automata theory studies the behavior of abstract machines, complexity theory investigates the resources needed to solve problems, and quantum computing takes advantage of the strange properties of quantum mechanics. By understanding the limitations imposed by each branch of computation theory, we can design more efficient algorithms and create faster computers.


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