Because of the algebraic properties above, many familiar and powerful tools of mathematics work in GF(2) just as well as other fields. For example, matrix operations, including
matrix inversion, can be applied to matrices with elements in GF(2) (
see matrix ring). Any
group (
V,+) with the property
v +
v = 0 for every
v in
V is necessarily
abelian and can be turned into a
vector space over GF(2) in a natural fashion, by defining 0
v = 0 and 1
v =
v for all
v in
V. This vector space will have a
basis, implying that the number of elements of
V must be a power of 2 (or infinite). In modern
computers, data are represented with
bit strings of a fixed length, called
machine words. These are endowed with the structure of a
vector space over GF(2). The addition of this vector space is the
bitwise operation called
XOR (exclusive or). The
bitwise AND is another operation on this vector space, which makes it a
Boolean algebra, a structure that underlies all
computer science. These spaces can also be augmented with a multiplication operation that makes them into a field GF(2
n), but the multiplication operation cannot be a bitwise operation. When
n is itself a power of two, the multiplication operation can be
nim-multiplication; alternatively, for any
n, one can use multiplication of polynomials over GF(2) modulo a
irreducible polynomial (as for instance for the field GF(28) in the description of the
Advanced Encryption Standard cipher).
Vector spaces and
polynomial rings over GF(2) are widely used in
coding theory, and in particular in
error correcting codes and modern
cryptography. For example, many common error correcting codes (such as
BCH codes) are
linear codes over GF(2) (codes defined from vector spaces over GF(2)), or
polynomial codes (codes defined as
quotients of polynomial rings over GF(2)). == Algebraic closure ==