In this work, we present a mathematical approach to classical and quantum error-correcting codes. Error-correcting codes are a sort of codes used in the communication systems in order to detect and correct errors that occur during the data transmission or storage due to noise and signal distortion, aiming to recover the original transmitted information in an efficient and safe way. Such codes basically add redundancies to the data, so that the detection and correction of erros are possible. Finally, we present a proposal applied to high school involving code theory, using the educational program Scratch.
In this work, we present a mathematical approach to classical and quantum error-correcting codes. Error-correcting codes are a sort of codes used in the communication systems in order to detect and correct errors that occur during the data transmission or storage due to noise and signal distortion, aiming to recover the original transmitted information in an efficient and safe way. Such codes basically add redundancies to the data, so that the detection and correction of erros are possible. Finally, we present a proposal applied to high school involving code theory, using the educational program Scratch.
Error-correcting codes are the means by which people compensate for interference in communication, and are essential for the accurate transmission and storage of digital data. All communication mechanisms and storage devices are subject to interference, typically called "noise," which corrupts communicated messages and stored data. Thus, for a communication system to faithfully transmit data, it must build redundancy into its transmissions in such a way that even if a transmission is partially corrupted, the intended message may be reconstructed. Error-correcting codes provide the mapping from messages to redundant transmissions. While a tremendous theoretical advance, more work is required before these codes can be used in practical communication systems. The decoding algorithms run in polynomial time, but need to be faster before they can be applied in practice.
In the preceding chapter we mentioned the inevitable errors that occur when one tries to send quantum signals over, say, an optical fiber, even when there is no eavesdropper. But errors in transmission are not a problem just for quantum cryptography. For this entire chapter we forget about sending quantum information and instead focus on simply transmitting ordinary data faithfully over some kind of channel. Moreover, we assume that the data either is not sensitive or has already been encrypted. Unfortunately, many methods for transmitting data are susceptible to outside influences that can cause errors. How do we protect information from these errors? Error-correcting codes provide a mathematical method of not only detecting these errors, but also correcting them. Nowadays error-correcting codes are ubiquitous; they are used, for example, in cell-phone transmissions and satellite links, in the representation of music on a compact disk, and even in the bar codes in grocery stores. The story of modern error-correcting codes began with Claude Shannon's famous paper “A Mathematical Theory of Communication,” which was published in 1948. Shannon worked for Bell Labs where he specialized in finding solutions to problems that arose in telephone communication. Quite naturally, he started considering ways to correct errors that occurred when information was transmitted over phone lines. Richard Hamming, who also worked at Bell Labs on this problem, published a groundbreaking paper in 1950 on the subject.
This report surveys quantum error-correcting codes. As Preskill claimed, 21st century would be the golden age of quantum error correction. Quantum channels behave differently from classical channels, so researchers face difficulties in developing robust quantum codes. Fortunately, the classical error control methods have been well developed. If we can learn many lessons from classical coding theory, we can expedite the development of quantum codes. Scientists have discovered that quantum error correction shares many concepts with classical counterpart. Both quantum and classical coding schemes add redundancy to information to protect against noises. They also have similar conditions for error detectability and correctability.
Barcode scans, clear phone calls, reliable data storage, satellite communication, and large-scale quantum computation are all made possible by error correction. We present a handbook version of The Error Correction Zoo, a curated reference of methods for protecting classical or quantum information from errors during storage and transmission. The handbook includes descriptions of these error-correcting codes and a classification according to the symbols they use. It also catalogues relations among codes and related objects such as sphere packings, lattices, designs, groups, and classical and quantum phases of matter. The collection is intended both as a rigorous reference and as a practical aid for tracing the web of code relationships and uncovering new connections.
A mathematical model of computation is an idealized abstraction. We design algorithms and perform analysis on the assumption that the mathematical operations we specify will be carried out exactly, and without error. Physical devices that implement an abstract model of computation are imperfect and of limited precision. For example, when a digital circuit is implemented on a physical circuit board, unwanted electrical noise in the environment may cause components to behave differently than expected, and may cause voltage levels (bit-values) to change. These sources of error must be controlled or compensated for, or else the resulting loss of efficiency may reduce the power of the information-processing device. If individual steps in a computation succeed with probability p, then a computation involving t sequential steps will have a success probability that decreases exponentially as pt. Although it may be impossible to eliminate the sources of errors, we can devise schemes to allow us to recover from errors using a reasonable amount of additional resources. Many classical digital computing devices use error-correcting codes to perform detection of and recovery from errors. The theory of error-correcting codes is itself a mathematical abstraction, but it is one that explicitly accounts for errors introduced by the imperfection and imprecision of realistic devices. This theory has proven extremely effective in allowing engineers to build computing devices that are resilient against errors. Quantum computers are more susceptible to errors than classical digital computers, because quantum mechanical systems are more delicate and more difficult to control. If large-scale quantum computers are to be possible, a theory of quantum error correction is needed. The discovery of quantum error correction has given researchers confidence that realistic large-scale quantum computing devices can be built despite the presence of errors. We begin by considering fundamental concepts for error correction in a classical setting. We will focus on three of these concepts: (a) the characterization of the error model, (b) the introduction of redundancy through encoding, and (c) an error recovery procedure. We will later see that these concepts generalize quite naturally for quantum error correction.
This is an expository article aiming to introduce the reader to the underlying mathematics and geometry of quantum error correction. Information stored on quantum particles is subject to noise and interference from the environment. Quantum error-correcting codes allow the negation of these effects in order to successfully restore the original quantum information. We briefly describe the necessary quantum-mechanical background to be able to understand how quantum error correction works. We go on to construct quantum codes: firstly qubit stabilizer codes, then qubit non-stabilizer codes, and finally codes with a higher local dimension. We will delve into the geometry of these codes. This allows one to deduce the parameters of the code efficiently, deduce the inequivalence between codes that have the same parameters, and presents a useful tool in deducing the feasibility of certain parameters. We also include sections on quantum maximum distance separable codes and the quantum MacWilliams identities.